Tuning A Storage System In Dependence Upon Workload Access Patterns

ABSTRACT

Performance tuning in a storage system that includes one or more storage devices, including: storing, by a primary controller of the storage system, data corresponding to one or more computer processes into one or more of the one or more storage devices, determining, by a secondary controller that is configured similarly to the primary controller, one or more utilization patterns of the data, and initiating, in dependence upon the one or more utilization patterns of the data, a modification to a manner in which the one or more computer processes access the data stored in the one or more storage devices.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of and claims priority from U.S. patent Ser. No. 10/007,459, issued Jun. 26, 2018, which claims the benefit of Provisional Patent Application Ser. No. 62/410,829, filed Oct. 20, 2016.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage in accordance with some implementations.

FIG. 1B illustrates a second example system for data storage in accordance with some implementations.

FIG. 1C illustrates a third example system for data storage in accordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage in accordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storage nodes and internal storage coupled to each storage node to provide network attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch coupling multiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storage node and contents of one of the non-volatile solid state storage units in accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments of the storage nodes and storage units of some previous figures in accordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane, compute and storage planes, and authorities interacting with underlying physical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storage cluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storage cluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled for data communications with a cloud services provider in accordance with some embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with some embodiments of the present disclosure.

FIG. 4 sets forth an example interface used to present analyzed utilization patterns in a storage array according to embodiments of the present disclosure.

FIG. 5 sets forth an example interface used to present analyzed utilization patterns in a storage array according to embodiments of the present disclosure.

FIG. 6 sets forth a flow chart illustrating an example method of analyzing utilization patterns in a storage array according to embodiments of the present disclosure.

FIG. 7 sets forth an example interface used to present analyzed utilization patterns in a storage array according to embodiments of the present disclosure.

FIG. 8 sets forth a flow chart illustrating an example method of analyzing utilization patterns in a storage array according to embodiments of the present disclosure.

FIG. 9 sets forth a flow chart illustrating an additional example method of performance tuning in a storage system that includes one or more storage devices according to some embodiments of the present disclosure.

FIG. 10 sets forth a flow chart illustrating an additional example method of performance tuning in a storage system that includes one or more storage devices according to some embodiments of the present disclosure.

FIG. 11 sets forth a flow chart illustrating an additional example method of performance tuning in a storage system that includes one or more storage devices according to some embodiments of the present disclosure.

FIG. 12 sets forth a flow chart illustrating an additional example method of performance tuning in a storage system that includes one or more storage devices according to some embodiments of the present disclosure.

FIG. 13 sets forth a flow chart illustrating an additional example method of performance tuning in a storage system that includes one or more storage devices according to some embodiments of the present disclosure.

FIG. 14 sets forth a flow chart illustrating an additional example method of performance tuning in a storage system that includes one or more storage devices according to some embodiments of the present disclosure.

FIG. 15 sets forth a flow chart illustrating an additional example method of performance tuning in a storage system that includes one or more storage devices according to some embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for performance tuning and analyzing utilization patterns in a storage system that includes one or more storage devices in accordance with embodiments of the present disclosure are described with reference to the accompanying drawings, beginning with FIG. 1A. FIG. 1A illustrates an example system for data storage, in accordance with some implementations. System 100 (also referred to as “storage system” herein) includes numerous elements for purposes of illustration rather than limitation. It may be noted that system 100 may include the same, more, or fewer elements configured in the same or different manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computing devices (also referred to as “client devices” herein) may be embodied, for example, a server in a data center, a workstation, a personal computer, a notebook, or the like. Computing devices 164A-B may be coupled for data communications to one or more storage arrays 102A-B through a storage area network (‘SAN’) 158 or a local area network (‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communications fabrics, devices, and protocols. For example, the fabrics for SAN 158 may include Fibre Channel, Ethernet, InfiniBand, Serial Attached Small Computer System Interface (‘SAS’), or the like. Data communications protocols for use with SAN 158 may include Advanced Technology Attachment (‘ATA’), Fibre Channel Protocol, Small Computer System Interface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’), HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or the like. It may be noted that SAN 158 is provided for illustration, rather than limitation. Other data communication couplings may be implemented between computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices, and protocols. For example, the fabrics for LAN 160 may include Ethernet (802.3), wireless (802.11), or the like. Data communication protocols for use in LAN 160 may include Transmission Control Protocol (‘TCP’), User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperText Transfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), Handheld Device Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’), Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for the computing devices 164A-B. Storage array 102A may be contained in a chassis (not shown), and storage array 102B may be contained in another chassis (not shown), in implementations. Storage array 102A and 102B may include one or more storage array controllers 110 (also referred to as “controller” herein). A storage array controller 110 may be embodied as a module of automated computing machinery comprising computer hardware, computer software, or a combination of computer hardware and software. In some implementations, the storage array controllers 110 may be configured to carry out various storage tasks. Storage tasks may include writing data received from the computing devices 164A-B to storage array 102A-B, erasing data from storage array 102A-B, retrieving data from storage array 102A-B and providing data to computing devices 164A-B, monitoring and reporting of disk utilization and performance, performing redundancy operations, such as Redundant Array of Independent Drives (‘RAID’) or RAID-like data redundancy operations, compressing data, encrypting data, and so forth.

Storage array controller 110 may be implemented in a variety of ways, including as a Field Programmable Gate Array (‘FPGA’), a Programmable Logic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’), System-on-Chip (‘SOC’), or any computing device that includes discrete components such as a processing device, central processing unit, computer memory, or various adapters. Storage array controller 110 may include, for example, a data communications adapter configured to support communications via the SAN 158 or LAN 160. In some implementations, storage array controller 110 may be independently coupled to the LAN 160. In implementations, storage array controller 110 may include an I/O controller or the like that couples the storage array controller 110 for data communications, through a midplane (not shown), to a persistent storage resource 170A-B (also referred to as a “storage resource” herein). The persistent storage resource 170A-B main include any number of storage drives 171A-F (also referred to as “storage devices” herein) and any number of non-volatile Random Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storage resource 170A-B may be configured to receive, from the storage array controller 110, data to be stored in the storage drives 171A-F. In some examples, the data may originate from computing devices 164A-B. In some examples, writing data to the NVRAM device may be carried out more quickly than directly writing data to the storage drive 171A-F. In implementations, the storage array controller 110 may be configured to utilize the NVRAM devices as a quickly accessible buffer for data destined to be written to the storage drives 171A-F. Latency for write requests using NVRAM devices as a buffer may be improved relative to a system in which a storage array controller 110 writes data directly to the storage drives 171A-F. In some implementations, the NVRAM devices may be implemented with computer memory in the form of high bandwidth, low latency RAM. The NVRAM device is referred to as “non-volatile” because the NVRAM device may receive or include a unique power source that maintains the state of the RAM after main power loss to the NVRAM device. Such a power source may be a battery, one or more capacitors, or the like. In response to a power loss, the NVRAM device may be configured to write the contents of the RAM to a persistent storage, such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any device configured to record data persistently, where “persistently” or “persistent” refers as to a device's ability to maintain recorded data after loss of power. In some implementations, storage drive 171A-F may correspond to non-disk storage media. For example, the storage drive 171A-F may be one or more solid-state drives (‘SSDs’), flash memory based storage, any type of solid-state non-volatile memory, or any other type of non-mechanical storage device. In other implementations, storage drive 171A-F may include may include mechanical or spinning hard disk, such as hard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110 may be configured for offloading device management responsibilities from storage drive 171A-F in storage array 102A-B. For example, storage array controllers 110 may manage control information that may describe the state of one or more memory blocks in the storage drives 171A-F. The control information may indicate, for example, that a particular memory block has failed and should no longer be written to, that a particular memory block contains boot code for a storage array controller 110, the number of program-erase (‘P/E’) cycles that have been performed on a particular memory block, the age of data stored in a particular memory block, the type of data that is stored in a particular memory block, and so forth. In some implementations, the control information may be stored with an associated memory block as metadata. In other implementations, the control information for the storage drives 171A-F may be stored in one or more particular memory blocks of the storage drives 171A-F that are selected by the storage array controller 110. The selected memory blocks may be tagged with an identifier indicating that the selected memory block contains control information. The identifier may be utilized by the storage array controllers 110 in conjunction with storage drives 171A-F to quickly identify the memory blocks that contain control information. For example, the storage controllers 110 may issue a command to locate memory blocks that contain control information. It may be noted that control information may be so large that parts of the control information may be stored in multiple locations, that the control information may be stored in multiple locations for purposes of redundancy, for example, or that the control information may otherwise be distributed across multiple memory blocks in the storage drive 171A-F.

In implementations, storage array controllers 110 may offload device management responsibilities from storage drives 171A-F of storage array 102A-B by retrieving, from the storage drives 171A-F, control information describing the state of one or more memory blocks in the storage drives 171A-F. Retrieving the control information from the storage drives 171A-F may be carried out, for example, by the storage array controller 110 querying the storage drives 171A-F for the location of control information for a particular storage drive 171A-F. The storage drives 171A-F may be configured to execute instructions that enable the storage drive 171A-F to identify the location of the control information. The instructions may be executed by a controller (not shown) associated with or otherwise located on the storage drive 171A-F and may cause the storage drive 171A-F to scan a portion of each memory block to identify the memory blocks that store control information for the storage drives 171A-F. The storage drives 171A-F may respond by sending a response message to the storage array controller 110 that includes the location of control information for the storage drive 171A-F. Responsive to receiving the response message, storage array controllers 110 may issue a request to read data stored at the address associated with the location of control information for the storage drives 171A-F.

In other implementations, the storage array controllers 110 may further offload device management responsibilities from storage drives 171A-F by performing, in response to receiving the control information, a storage drive management operation. A storage drive management operation may include, for example, an operation that is typically performed by the storage drive 171A-F (e.g., the controller (not shown) associated with a particular storage drive 171A-F). A storage drive management operation may include, for example, ensuring that data is not written to failed memory blocks within the storage drive 171A-F, ensuring that data is written to memory blocks within the storage drive 171A-F in such a way that adequate wear leveling is achieved, and so forth.

In implementations, storage array 102A-B may implement two or more storage array controllers 110. For example, storage array 102A may include storage array controllers 110A and storage array controllers 110B. At a given instance, a single storage array controller 110 (e.g., storage array controller 110A) of a storage system 100 may be designated with primary status (also referred to as “primary controller” herein), and other storage array controllers 110 (e.g., storage array controller 110A) may be designated with secondary status (also referred to as “secondary controller” herein). The primary controller may have particular rights, such as permission to alter data in persistent storage resource 170A-B (e.g., writing data to persistent storage resource 170A-B). At least some of the rights of the primary controller may supersede the rights of the secondary controller. For instance, the secondary controller may not have permission to alter data in persistent storage resource 170A-B when the primary controller has the right. The status of storage array controllers 110 may change. For example, storage array controller 110A may be designated with secondary status, and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage array controller 110A, may serve as the primary controller for one or more storage arrays 102A-B, and a second controller, such as storage array controller 110B, may serve as the secondary controller for the one or more storage arrays 102A-B. For example, storage array controller 110A may be the primary controller for storage array 102A and storage array 102B, and storage array controller 110B may be the secondary controller for storage array 102A and 102B. In some implementations, storage array controllers 110C and 110D (also referred to as “storage processing modules”) may neither have primary or secondary status. Storage array controllers 110C and 110D, implemented as storage processing modules, may act as a communication interface between the primary and secondary controllers (e.g., storage array controllers 110A and 110B, respectively) and storage array 102B. For example, storage array controller 110A of storage array 102A may send a write request, via SAN 158, to storage array 102B. The write request may be received by both storage array controllers 110C and 110D of storage array 102B. Storage array controllers 110C and 110D facilitate the communication, e.g., send the write request to the appropriate storage drive 171A-F. It may be noted that in some implementations storage processing modules may be used to increase the number of storage drives controlled by the primary and secondary controllers.

In implementations, storage array controllers 110 are communicatively coupled, via a midplane (not shown), to one or more storage drives 171A-F and to one or more NVRAM devices (not shown) that are included as part of a storage array 102A-B. The storage array controllers 110 may be coupled to the midplane via one or more data communication links and the midplane may be coupled to the storage drives 171A-F and the NVRAM devices via one or more data communications links. The data communications links described herein are collectively illustrated by data communications links 108A-D and may include a Peripheral Component Interconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordance with some implementations. Storage array controller 101 illustrated in FIG. 1B may similar to the storage array controllers 110 described with respect to FIG. 1A. In one example, storage array controller 101 may be similar to storage array controller 110A or storage array controller 110B. Storage array controller 101 includes numerous elements for purposes of illustration rather than limitation. It may be noted that storage array controller 101 may include the same, more, or fewer elements configured in the same or different manner in other implementations. It may be noted that elements of FIG. 1A may be included below to help illustrate features of storage array controller 101.

Storage array controller 101 may include one or more processing devices 104 and random access memory (‘RAM’) 111. Processing device 104 (or controller 101) represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 104 (or controller 101) may be a complex instruction set computing (‘CISC’) microprocessor, reduced instruction set computing (‘RISC’) microprocessor, very long instruction word (‘VLIW’) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 104 (or controller 101) may also be one or more special-purpose processing devices such as an application specific integrated circuit (‘ASIC’), a field programmable gate array (‘FPGA’), a digital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a data communications link 106, which may be embodied as a high speed memory bus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is an operating system 112. In some implementations, instructions 113 are stored in RAM 111. Instructions 113 may include computer program instructions for performing operations in a direct-mapped flash storage system. In one embodiment, a direct-mapped flash storage system is one that that addresses data blocks within flash drives directly and without an address translation performed by the storage controllers of the flash drives.

In implementations, storage array controller 101 includes one or more host bus adapters 103A-C that are coupled to the processing device 104 via a data communications link 105A-C. In implementations, host bus adapters 103A-C may be computer hardware that connects a host system (e.g., the storage array controller) to other network and storage arrays. In some examples, host bus adapters 103A-C may be a Fibre Channel adapter that enables the storage array controller 101 to connect to a SAN, an Ethernet adapter that enables the storage array controller 101 to connect to a LAN, or the like. Host bus adapters 103A-C may be coupled to the processing device 104 via a data communications link 105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host bus adapter 114 that is coupled to an expander 115. The expander 115 may be used to attach a host system to a larger number of storage drives. The expander 115 may, for example, be a SAS expander utilized to enable the host bus adapter 114 to attach to storage drives in an implementation where the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch 116 coupled to the processing device 104 via a data communications link 109. The switch 116 may be a computer hardware device that can create multiple endpoints out of a single endpoint, thereby enabling multiple devices to share a single endpoint. The switch 116 may, for example, be a PCIe switch that is coupled to a PCIe bus (e.g., data communications link 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a data communications link 107 for coupling the storage array controller 101 to other storage array controllers. In some examples, data communications link 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives may implement a process across the flash drives that are part of the traditional storage system. For example, a higher level process of the storage system may initiate and control a process across the flash drives. However, a flash drive of the traditional storage system may include its own storage controller that also performs the process. Thus, for the traditional storage system, a higher level process (e.g., initiated by the storage system) and a lower level process (e.g., initiated by a storage controller of the storage system) may both be performed.

To resolve various deficiencies of a traditional storage system, operations may be performed by higher level processes and not by the lower level processes. For example, the flash storage system may include flash drives that do not include storage controllers that provide the process. Thus, the operating system of the flash storage system itself may initiate and control the process. This may be accomplished by a direct-mapped flash storage system that addresses data blocks within the flash drives directly and without an address translation performed by the storage controllers of the flash drives.

The operating system of the flash storage system may identify and maintain a list of allocation units across multiple flash drives of the flash storage system. The allocation units may be entire erase blocks or multiple erase blocks. The operating system may maintain a map or address range that directly maps addresses to erase blocks of the flash drives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used to rewrite data and erase data. For example, the operations may be performed on one or more allocation units that include a first data and a second data where the first data is to be retained and the second data is no longer being used by the flash storage system. The operating system may initiate the process to write the first data to new locations within other allocation units and erasing the second data and marking the allocation units as being available for use for subsequent data. Thus, the process may only be performed by the higher level operating system of the flash storage system without an additional lower level process being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating system of the flash storage system include increased reliability of the flash drives of the flash storage system as unnecessary or redundant write operations are not being performed during the process. One possible point of novelty here is the concept of initiating and controlling the process at the operating system of the flash storage system. In addition, the process can be controlled by the operating system across multiple flash drives. This is contrast to the process being performed by a storage controller of a flash drive.

A storage system can consist of two storage array controllers that share a set of drives for failover purposes, or it could consist of a single storage array controller that provides a storage service that utilizes multiple drives, or it could consist of a distributed network of storage array controllers each with some number of drives or some amount of Flash storage where the storage array controllers in the network collaborate to provide a complete storage service and collaborate on various aspects of a storage service including storage allocation and garbage collection.

FIG. 1C illustrates a third example system 117 for data storage in accordance with some implementations. System 117 (also referred to as “storage system” herein) includes numerous elements for purposes of illustration rather than limitation. It may be noted that system 117 may include the same, more, or fewer elements configured in the same or different manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral Component Interconnect (‘PCI’) flash storage device 118 with separately addressable fast write storage. System 117 may include a storage controller 119. In one embodiment, storage controller 119 may be a CPU, ASIC, FPGA, or any other circuitry that may implement control structures necessary according to the present disclosure. In one embodiment, system 117 includes flash memory devices (e.g., including flash memory devices 120 a-n), operatively coupled to various channels of the storage device controller 119. Flash memory devices 120 a-n, may be presented to the controller 119 as an addressable collection of Flash pages, erase blocks, and/or control elements sufficient to allow the storage device controller 119 to program and retrieve various aspects of the Flash. In one embodiment, storage device controller 119 may perform operations on flash memory devices 120A-N including storing and retrieving data content of pages, arranging and erasing any blocks, tracking statistics related to the use and reuse of Flash memory pages, erase blocks, and cells, tracking and predicting error codes and faults within the Flash memory, controlling voltage levels associated with programming and retrieving contents of Flash cells, etc. In one embodiment, system 117 may include RAM 121 to store separately addressable fast-write data. In one embodiment, RAM 121 may be one or more separate discrete devices. In another embodiment, RAM 121 may be integrated into storage device controller 119 or multiple storage device controllers. The RAM 121 may be utilized for other purposes as well, such as temporary program memory for a processing device (e.g., a CPU) in the storage device controller 119.

In one embodiment, system 119 may include a stored energy device 122, such as a rechargeable battery or a capacitor. Stored energy device 122 may store energy sufficient to power the storage device controller 119, some amount of the RAM (e.g., RAM 121), and some amount of Flash memory (e.g., Flash memory 120 a-120 n) for sufficient time to write the contents of RAM to Flash memory. In one embodiment, storage device controller 119 may write the contents of RAM to Flash Memory if the storage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123 a, 123 b. In one embodiment, data communications links 123 a, 123 b may be PCI interfaces. In another embodiment, data communications links 123 a, 123 b may be based on other communications standards (e.g., HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123 b may be based on non-volatile memory express (‘NVMe’) or NVMe over fabrics (‘NVMf’) specifications that allow external connection to the storage device controller 119 from other components in the storage system 117. It should be noted that data communications links may be interchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), which may be provided over one or both data communications links 123 a, 123 b, or which may be provided separately. An alternative embodiment includes a separate Flash memory (not shown) dedicated for use in storing the content of RAM 121. The storage device controller 119 may present a logical device over a PCI bus which may include an addressable fast-write logical device, or a distinct part of the logical address space of the storage device 118, which may be presented as PCI memory or as persistent storage. In one embodiment, operations to store into the device are directed into the RAM 121. On power failure, the storage device controller 119 may write stored content associated with the addressable fast-write logical storage to Flash memory (e.g., Flash memory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation of some or all of the content of the Flash memory devices 120 a-n, where that presentation allows a storage system including a storage device 118 (e.g., storage system 117) to directly address Flash memory pages and directly reprogram erase blocks from storage system components that are external to the storage device through the PCI bus. The presentation may also allow one or more of the external components to control and retrieve other aspects of the Flash memory including some or all of: tracking statistics related to use and reuse of Flash memory pages, erase blocks, and cells across all the Flash memory devices; tracking and predicting error codes and faults within and across the Flash memory devices; controlling voltage levels associated with programming and retrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient to ensure completion of in-progress operations to the Flash memory devices 107 a-120 n stored energy device 122 may power storage device controller 119 and associated Flash memory devices (e.g., 120 a-n) for those operations, as well as for the storing of fast-write RAM to Flash memory. Stored energy device 122 may be used to store accumulated statistics and other parameters kept and tracked by the Flash memory devices 120 a-n and/or the storage device controller 119. Separate capacitors or stored energy devices (such as smaller capacitors near or embedded within the Flash memory devices themselves) may be used for some or all of the operations described herein.

Various schemes may be used to track and optimize the life span of the stored energy component, such as adjusting voltage levels over time, partially discharging the storage energy device 122 to measure corresponding discharge characteristics, etc. If the available energy decreases over time, the effective available capacity of the addressable fast-write storage may be decreased to ensure that it can be written safely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage in accordance with some implementations. In one embodiment, system 124 includes storage controllers 125 a, 125 b. In one embodiment, storage controllers 125 a, 125 b are operatively coupled to Dual PCI storage devices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers 125 a, 125 b may be operatively coupled (e.g., via a storage network 130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b) provide storage services, such as a SCS) block storage array, a file server, an object server, a database or data analytics service, etc. The storage controllers 125 a, 125 b may provide services through some number of network interfaces (e.g., 126 a-d) to host computers 127 a-n outside of the storage system 124. Storage controllers 125 a, 125 b may provide integrated services or an application entirely within the storage system 124, forming a converged storage and compute system. The storage controllers 125 a, 125 b may utilize the fast write memory within or across storage devices 119 a-d to journal in progress operations to ensure the operations are not lost on a power failure, storage controller removal, storage controller or storage system shutdown, or some fault of one or more software or hardware components within the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters to one or the other PCI buses 128 a, 128 b. In another embodiment, 128 a and 128 b may be based on other communications standards (e.g., HyperTransport, InfiniBand, etc.). Other storage system embodiments may operate storage controllers 125 a, 125 b as multi-masters for both PCI buses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switching infrastructure or fabric may connect multiple storage controllers. Some storage system embodiments may allow storage devices to communicate with each other directly rather than communicating only with storage controllers. In one embodiment, a storage device controller 119 a may be operable under direction from a storage controller 125 a to synthesize and transfer data to be stored into Flash memory devices from data that has been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, a recalculated version of RAM content may be transferred after a storage controller has determined that an operation has fully committed across the storage system, or when fast-write memory on the device has reached a certain used capacity, or after a certain amount of time, to ensure improve safety of the data or to release addressable fast-write capacity for reuse. This mechanism may be used, for example, to avoid a second transfer over a bus (e.g., 128 a, 128 b) from the storage controllers 125 a, 125 b. In one embodiment, a recalculation may include compressing data, attaching indexing or other metadata, combining multiple data segments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125 b, a storage device controller 119 a, 119 b may be operable to calculate and transfer data to other storage devices from data stored in RAM (e.g., RAM 121 of FIG. 1C) without involvement of the storage controllers 125 a, 125 b. This operation may be used to mirror data stored in one controller 125 a to another controller 125 b, or it could be used to offload compression, data aggregation, and/or erasure coding calculations and transfers to storage devices to reduce load on storage controllers or the storage controller interface 129 a, 129 b to the PCI bus 128 a, 128 b.

A storage device controller 119 may include mechanisms for implementing high availability primitives for use by other parts of a storage system external to the Dual PCI storage device 118. For example, reservation or exclusion primitives may be provided so that, in a storage system with two storage controllers providing a highly available storage service, one storage controller may prevent the other storage controller from accessing or continuing to access the storage device. This could be used, for example, in cases where one controller detects that the other controller is not functioning properly or where the interconnect between the two storage controllers may itself not be functioning properly.

In one embodiment, a storage system for use with Dual PCI direct mapped storage devices with separately addressable fast write storage includes systems that manage erase blocks or groups of erase blocks as allocation units for storing data on behalf of the storage service, or for storing metadata (e.g., indexes, logs, etc.) associated with the storage service, or for proper management of the storage system itself. Flash pages, which may be a few kilobytes in size, may be written as data arrives or as the storage system is to persist data for long intervals of time (e.g., above a defined threshold of time). To commit data more quickly, or to reduce the number of writes to the Flash memory devices, the storage controllers may first write data into the separately addressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate the use of erase blocks within and across storage devices (e.g., 118) in accordance with an age and expected remaining lifespan of the storage devices, or based on other statistics. The storage controllers 125 a, 125 b may initiate garbage collection and data migration data between storage devices in accordance with pages that are no longer needed as well as to manage Flash page and erase block lifespans and to manage overall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/or erasure coding schemes as part of storing data into addressable fast write storage and/or as part of writing data into allocation units associated with erase blocks. Erasure codes may be used across storage devices, as well as within erase blocks or allocation units, or within and across Flash memory devices on a single storage device, to provide redundancy against single or multiple storage device failures or to protect against internal corruptions of Flash memory pages resulting from Flash memory operations or from degradation of Flash memory cells. Mirroring and erasure coding at various levels may be used to recover from multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate a storage cluster that stores user data, such as user data originating from one or more user or client systems or other sources external to the storage cluster. The storage cluster distributes user data across storage nodes housed within a chassis, or across multiple chassis, using erasure coding and redundant copies of metadata. Erasure coding refers to a method of data protection or reconstruction in which data is stored across a set of different locations, such as disks, storage nodes or geographic locations. Flash memory is one type of solid-state memory that may be integrated with the embodiments, although the embodiments may be extended to other types of solid-state memory or other storage medium, including non-solid state memory. Control of storage locations and workloads are distributed across the storage locations in a clustered peer-to-peer system. Tasks such as mediating communications between the various storage nodes, detecting when a storage node has become unavailable, and balancing I/Os (inputs and outputs) across the various storage nodes, are all handled on a distributed basis. Data is laid out or distributed across multiple storage nodes in data fragments or stripes that support data recovery in some embodiments. Ownership of data can be reassigned within a cluster, independent of input and output patterns. This architecture described in more detail below allows a storage node in the cluster to fail, with the system remaining operational, since the data can be reconstructed from other storage nodes and thus remain available for input and output operations. In various embodiments, a storage node may be referred to as a cluster node, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., an enclosure housing one or more storage nodes. A mechanism to provide power to each storage node, such as a power distribution bus, and a communication mechanism, such as a communication bus that enables communication between the storage nodes are included within the chassis. The storage cluster can run as an independent system in one location according to some embodiments. In one embodiment, a chassis contains at least two instances of both the power distribution and the communication bus which may be enabled or disabled independently. The internal communication bus may be an Ethernet bus, however, other technologies such as PCIe, InfiniBand, and others, are equally suitable. The chassis provides a port for an external communication bus for enabling communication between multiple chassis, directly or through a switch, and with client systems. The external communication may use a technology such as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments, the external communication bus uses different communication bus technologies for inter-chassis and client communication. If a switch is deployed within or between chassis, the switch may act as a translation between multiple protocols or technologies. When multiple chassis are connected to define a storage cluster, the storage cluster may be accessed by a client using either proprietary interfaces or standard interfaces such as network file system (‘NFS’), common internet file system (‘CIFS’), small computer system interface (‘SCSI’) or hypertext transfer protocol (‘HTTP’). Translation from the client protocol may occur at the switch, chassis external communication bus or within each storage node. In some embodiments, multiple chassis may be coupled or connected to each other through an aggregator switch. A portion and/or all of the coupled or connected chassis may be designated as a storage cluster. As discussed above, each chassis can have multiple blades, each blade has a media access control (‘MAC’) address, but the storage cluster is presented to an external network as having a single cluster IP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storage server is connected to one or more non-volatile solid state memory units, which may be referred to as storage units or storage devices. One embodiment includes a single storage server in each storage node and between one to eight non-volatile solid state memory units, however this one example is not meant to be limiting. The storage server may include a processor, DRAM and interfaces for the internal communication bus and power distribution for each of the power buses. Inside the storage node, the interfaces and storage unit share a communication bus, e.g., PCI Express, in some embodiments. The non-volatile solid state memory units may directly access the internal communication bus interface through a storage node communication bus, or request the storage node to access the bus interface. The non-volatile solid state memory unit contains an embedded CPU, solid state storage controller, and a quantity of solid state mass storage, e.g., between 2-32 terabytes (‘TB’) in some embodiments. An embedded volatile storage medium, such as DRAM, and an energy reserve apparatus are included in the non-volatile solid state memory unit. In some embodiments, the energy reserve apparatus is a capacitor, super-capacitor, or battery that enables transferring a subset of DRAM contents to a stable storage medium in the case of power loss. In some embodiments, the non-volatile solid state memory unit is constructed with a storage class memory, such as phase change or magnetoresistive random access memory (‘MRAM’) that substitutes for DRAM and enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid state storage is the ability to proactively rebuild data in a storage cluster. The storage nodes and non-volatile solid state storage can determine when a storage node or non-volatile solid state storage in the storage cluster is unreachable, independent of whether there is an attempt to read data involving that storage node or non-volatile solid state storage. The storage nodes and non-volatile solid state storage then cooperate to recover and rebuild the data in at least partially new locations. This constitutes a proactive rebuild, in that the system rebuilds data without waiting until the data is needed for a read access initiated from a client system employing the storage cluster. These and further details of the storage memory and operation thereof are discussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiple storage nodes 150 and internal solid-state memory coupled to each storage node to provide network attached storage or storage area network, in accordance with some embodiments. A network attached storage, storage area network, or a storage cluster, or other storage memory, could include one or more storage clusters 161, each having one or more storage nodes 150, in a flexible and reconfigurable arrangement of both the physical components and the amount of storage memory provided thereby. The storage cluster 161 is designed to fit in a rack, and one or more racks can be set up and populated as desired for the storage memory. The storage cluster 161 has a chassis 138 having multiple slots 142. It should be appreciated that chassis 138 may be referred to as a housing, enclosure, or rack unit. In one embodiment, the chassis 138 has fourteen slots 142, although other numbers of slots are readily devised. For example, some embodiments have four slots, eight slots, sixteen slots, thirty-two slots, or other suitable number of slots. Each slot 142 can accommodate one storage node 150 in some embodiments. Chassis 138 includes flaps 148 that can be utilized to mount the chassis 138 on a rack. Fans 144 provide air circulation for cooling of the storage nodes 150 and components thereof, although other cooling components could be used, or an embodiment could be devised without cooling components. A switch fabric 146 couples storage nodes 150 within chassis 138 together and to a network for communication to the memory. In an embodiment depicted in herein, the slots 142 to the left of the switch fabric 146 and fans 144 are shown occupied by storage nodes 150, while the slots 142 to the right of the switch fabric 146 and fans 144 are empty and available for insertion of storage node 150 for illustrative purposes. This configuration is one example, and one or more storage nodes 150 could occupy the slots 142 in various further arrangements. The storage node arrangements need not be sequential or adjacent in some embodiments. Storage nodes 150 are hot pluggable, meaning that a storage node 150 can be inserted into a slot 142 in the chassis 138, or removed from a slot 142, without stopping or powering down the system. Upon insertion or removal of storage node 150 from slot 142, the system automatically reconfigures in order to recognize and adapt to the change. Reconfiguration, in some embodiments, includes restoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodiment shown here, the storage node 150 includes a printed circuit board 159 populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU 156, and a non-volatile solid state storage 152 coupled to the CPU 156, although other mountings and/or components could be used in further embodiments. The memory 154 has instructions which are executed by the CPU 156 and/or data operated on by the CPU 156. As further explained below, the non-volatile solid state storage 152 includes flash or, in further embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning that storage capacity with non-uniform storage sizes is readily added, as described above. One or more storage nodes 150 can be plugged into or removed from each chassis and the storage cluster self-configures in some embodiments. Plug-in storage nodes 150, whether installed in a chassis as delivered or later added, can have different sizes. For example, in one embodiment a storage node 150 can have any multiple of 4 TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, a storage node 150 could have any multiple of other storage amounts or capacities. Storage capacity of each storage node 150 is broadcast, and influences decisions of how to stripe the data. For maximum storage efficiency, an embodiment can self-configure as wide as possible in the stripe, subject to a predetermined requirement of continued operation with loss of up to one, or up to two, non-volatile solid state storage units 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 171 and power distribution bus 172 coupling multiple storage nodes 150. Referring back to FIG. 2A, the communications interconnect 171 can be included in or implemented with the switch fabric 146 in some embodiments. Where multiple storage clusters 161 occupy a rack, the communications interconnect 171 can be included in or implemented with a top of rack switch, in some embodiments. As illustrated in FIG. 2B, storage cluster 161 is enclosed within a single chassis 138. External port 176 is coupled to storage nodes 150 through communications interconnect 171, while external port 174 is coupled directly to a storage node. External power port 178 is coupled to power distribution bus 172. Storage nodes 150 may include varying amounts and differing capacities of non-volatile solid state storage 152 as described with reference to FIG. 2A. In addition, one or more storage nodes 150 may be a compute only storage node as illustrated in FIG. 2B. Authorities 168 are implemented on the non-volatile solid state storages 152, for example as lists or other data structures stored in memory. In some embodiments the authorities are stored within the non-volatile solid state storage 152 and supported by software executing on a controller or other processor of the non-volatile solid state storage 152. In a further embodiment, authorities 168 are implemented on the storage nodes 150, for example as lists or other data structures stored in the memory 154 and supported by software executing on the CPU 156 of the storage node 150. Authorities 168 control how and where data is stored in the non-volatile solid state storages 152 in some embodiments. This control assists in determining which type of erasure coding scheme is applied to the data, and which storage nodes 150 have which portions of the data. Each authority 168 may be assigned to a non-volatile solid state storage 152. Each authority may control a range of inode numbers, segment numbers, or other data identifiers which are assigned to data by a file system, by the storage nodes 150, or by the non-volatile solid state storage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in the system in some embodiments. In addition, every piece of data and every piece of metadata has an owner, which may be referred to as an authority. If that authority is unreachable, for example through failure of a storage node, there is a plan of succession for how to find that data or that metadata. In various embodiments, there are redundant copies of authorities 168. Authorities 168 have a relationship to storage nodes 150 and non-volatile solid state storage 152 in some embodiments. Each authority 168, covering a range of data segment numbers or other identifiers of the data, may be assigned to a specific non-volatile solid state storage 152. In some embodiments the authorities 168 for all of such ranges are distributed over the non-volatile solid state storages 152 of a storage cluster. Each storage node 150 has a network port that provides access to the non-volatile solid state storage(s) 152 of that storage node 150. Data can be stored in a segment, which is associated with a segment number and that segment number is an indirection for a configuration of a RAID (redundant array of independent disks) stripe in some embodiments. The assignment and use of the authorities 168 thus establishes an indirection to data. Indirection may be referred to as the ability to reference data indirectly, in this case via an authority 168, in accordance with some embodiments. A segment identifies a set of non-volatile solid state storage 152 and a local identifier into the set of non-volatile solid state storage 152 that may contain data. In some embodiments, the local identifier is an offset into the device and may be reused sequentially by multiple segments. In other embodiments the local identifier is unique for a specific segment and never reused. The offsets in the non-volatile solid state storage 152 are applied to locating data for writing to or reading from the non-volatile solid state storage 152 (in the form of a RAID stripe). Data is striped across multiple units of non-volatile solid state storage 152, which may include or be different from the non-volatile solid state storage 152 having the authority 168 for a particular data segment.

If there is a change in where a particular segment of data is located, e.g., during a data move or a data reconstruction, the authority 168 for that data segment should be consulted, at that non-volatile solid state storage 152 or storage node 150 having that authority 168. In order to locate a particular piece of data, embodiments calculate a hash value for a data segment or apply an inode number or a data segment number. The output of this operation points to a non-volatile solid state storage 152 having the authority 168 for that particular piece of data. In some embodiments there are two stages to this operation. The first stage maps an entity identifier (ID), e.g., a segment number, inode number, or directory number to an authority identifier. This mapping may include a calculation such as a hash or a bit mask. The second stage is mapping the authority identifier to a particular non-volatile solid state storage 152, which may be done through an explicit mapping. The operation is repeatable, so that when the calculation is performed, the result of the calculation repeatably and reliably points to a particular non-volatile solid state storage 152 having that authority 168. The operation may include the set of reachable storage nodes as input. If the set of reachable non-volatile solid state storage units changes the optimal set changes. In some embodiments, the persisted value is the current assignment (which is always true) and the calculated value is the target assignment the cluster will attempt to reconfigure towards. This calculation may be used to determine the optimal non-volatile solid state storage 152 for an authority in the presence of a set of non-volatile solid state storage 152 that are reachable and constitute the same cluster. The calculation also determines an ordered set of peer non-volatile solid state storage 152 that will also record the authority to non-volatile solid state storage mapping so that the authority may be determined even if the assigned non-volatile solid state storage is unreachable. A duplicate or substitute authority 168 may be consulted if a specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156 on a storage node 150 are to break up write data, and reassemble read data. When the system has determined that data is to be written, the authority 168 for that data is located as above. When the segment ID for data is already determined the request to write is forwarded to the non-volatile solid state storage 152 currently determined to be the host of the authority 168 determined from the segment. The host CPU 156 of the storage node 150, on which the non-volatile solid state storage 152 and corresponding authority 168 reside, then breaks up or shards the data and transmits the data out to various non-volatile solid state storage 152. The transmitted data is written as a data stripe in accordance with an erasure coding scheme. In some embodiments, data is requested to be pulled, and in other embodiments, data is pushed. In reverse, when data is read, the authority 168 for the segment ID containing the data is located as described above. The host CPU 156 of the storage node 150 on which the non-volatile solid state storage 152 and corresponding authority 168 reside requests the data from the non-volatile solid state storage and corresponding storage nodes pointed to by the authority. In some embodiments the data is read from flash storage as a data stripe. The host CPU 156 of storage node 150 then reassembles the read data, correcting any errors (if present) according to the appropriate erasure coding scheme, and forwards the reassembled data to the network. In further embodiments, some or all of these tasks can be handled in the non-volatile solid state storage 152. In some embodiments, the segment host requests the data be sent to storage node 150 by requesting pages from storage and then sending the data to the storage node making the original request.

In some systems, for example in UNIX-style file systems, data is handled with an index node or inode, which specifies a data structure that represents an object in a file system. The object could be a file or a directory, for example. Metadata may accompany the object, as attributes such as permission data and a creation timestamp, among other attributes. A segment number could be assigned to all or a portion of such an object in a file system. In other systems, data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system. Entities are grouped into sets called authorities. Each authority has an authority owner, which is a storage node that has the exclusive right to update the entities in the authority. In other words, a storage node contains the authority, and that the authority, in turn, contains entities.

A segment is a logical container of data in accordance with some embodiments. A segment is an address space between medium address space and physical flash locations, i.e., the data segment number, are in this address space. Segments may also contain meta-data, which enable data redundancy to be restored (rewritten to different flash locations or devices) without the involvement of higher level software. In one embodiment, an internal format of a segment contains client data and medium mappings to determine the position of that data. Each data segment is protected, e.g., from memory and other failures, by breaking the segment into a number of data and parity shards, where applicable. The data and parity shards are distributed, i.e., striped, across non-volatile solid state storage 152 coupled to the host CPUs 156 (See FIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage of the term segments refers to the container and its place in the address space of segments in some embodiments. Usage of the term stripe refers to the same set of shards as a segment and includes how the shards are distributed along with redundancy or parity information in accordance with some embodiments.

A series of address-space transformations takes place across an entire storage system. At the top are the directory entries (file names) which link to an inode. Inodes point into medium address space, where data is logically stored. Medium addresses may be mapped through a series of indirect mediums to spread the load of large files, or implement data services like deduplication or snapshots. Medium addresses may be mapped through a series of indirect mediums to spread the load of large files, or implement data services like deduplication or snapshots. Segment addresses are then translated into physical flash locations. Physical flash locations have an address range bounded by the amount of flash in the system in accordance with some embodiments. Medium addresses and segment addresses are logical containers, and in some embodiments use a 128 bit or larger identifier so as to be practically infinite, with a likelihood of reuse calculated as longer than the expected life of the system. Addresses from logical containers are allocated in a hierarchical fashion in some embodiments. Initially, each non-volatile solid state storage unit 152 may be assigned a range of address space. Within this assigned range, the non-volatile solid state storage 152 is able to allocate addresses without synchronization with other non-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts that are optimized for varying workload patterns and storage devices. These layouts incorporate multiple redundancy schemes, compression formats and index algorithms. Some of these layouts store information about authorities and authority masters, while others store file metadata and file data. The redundancy schemes include error correction codes that tolerate corrupted bits within a single storage device (such as a NAND flash chip), erasure codes that tolerate the failure of multiple storage nodes, and replication schemes that tolerate data center or regional failures. In some embodiments, low density parity check (‘LDPC’) code is used within a single storage unit. Reed-Solomon encoding is used within a storage cluster, and mirroring is used within a storage grid in some embodiments. Metadata may be stored using an ordered log structured index (such as a Log Structured Merge Tree), and large data may not be stored in a log structured layout.

In order to maintain consistency across multiple copies of an entity, the storage nodes agree implicitly on two things through calculations: (1) the authority that contains the entity, and (2) the storage node that contains the authority. The assignment of entities to authorities can be done by pseudo randomly assigning entities to authorities, by splitting entities into ranges based upon an externally produced key, or by placing a single entity into each authority. Examples of pseudorandom schemes are linear hashing and the Replication Under Scalable Hashing (‘RUSH’) family of hashes, including Controlled Replication Under Scalable Hashing (‘CRUSH’). In some embodiments, pseudo-random assignment is utilized only for assigning authorities to nodes because the set of nodes can change. The set of authorities cannot change so any subjective function may be applied in these embodiments. Some placement schemes automatically place authorities on storage nodes, while other placement schemes rely on an explicit mapping of authorities to storage nodes. In some embodiments, a pseudorandom scheme is utilized to map from each authority to a set of candidate authority owners. A pseudorandom data distribution function related to CRUSH may assign authorities to storage nodes and create a list of where the authorities are assigned. Each storage node has a copy of the pseudorandom data distribution function, and can arrive at the same calculation for distributing, and later finding or locating an authority. Each of the pseudorandom schemes requires the reachable set of storage nodes as input in some embodiments in order to conclude the same target nodes. Once an entity has been placed in an authority, the entity may be stored on physical devices so that no expected failure will lead to unexpected data loss. In some embodiments, rebalancing algorithms attempt to store the copies of all entities within an authority in the same layout and on the same set of machines.

Examples of expected failures include device failures, stolen machines, datacenter fires, and regional disasters, such as nuclear or geological events. Different failures lead to different levels of acceptable data loss. In some embodiments, a stolen storage node impacts neither the security nor the reliability of the system, while depending on system configuration, a regional event could lead to no loss of data, a few seconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy is independent of the placement of authorities for data consistency. In some embodiments, storage nodes that contain authorities do not contain any persistent storage. Instead, the storage nodes are connected to non-volatile solid state storage units that do not contain authorities. The communications interconnect between storage nodes and non-volatile solid state storage units consists of multiple communication technologies and has non-uniform performance and fault tolerance characteristics. In some embodiments, as mentioned above, non-volatile solid state storage units are connected to storage nodes via PCI express, storage nodes are connected together within a single chassis using Ethernet backplane, and chassis are connected together to form a storage cluster. Storage clusters are connected to clients using Ethernet or fiber channel in some embodiments. If multiple storage clusters are configured into a storage grid, the multiple storage clusters are connected using the Internet or other long-distance networking links, such as a “metro scale” link or private link that does not traverse the internet.

Authority owners have the exclusive right to modify entities, to migrate entities from one non-volatile solid state storage unit to another non-volatile solid state storage unit, and to add and remove copies of entities. This allows for maintaining the redundancy of the underlying data. When an authority owner fails, is going to be decommissioned, or is overloaded, the authority is transferred to a new storage node. Transient failures make it non-trivial to ensure that all non-faulty machines agree upon the new authority location. The ambiguity that arises due to transient failures can be achieved automatically by a consensus protocol such as Paxos, hot-warm failover schemes, via manual intervention by a remote system administrator, or by a local hardware administrator (such as by physically removing the failed machine from the cluster, or pressing a button on the failed machine). In some embodiments, a consensus protocol is used, and failover is automatic. If too many failures or replication events occur in too short a time period, the system goes into a self-preservation mode and halts replication and data movement activities until an administrator intervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authority owners update entities in their authorities, the system transfers messages between the storage nodes and non-volatile solid state storage units. With regard to persistent messages, messages that have different purposes are of different types. Depending on the type of the message, the system maintains different ordering and durability guarantees. As the persistent messages are being processed, the messages are temporarily stored in multiple durable and non-durable storage hardware technologies. In some embodiments, messages are stored in RAM, NVRAM and on NAND flash devices, and a variety of protocols are used in order to make efficient use of each storage medium. Latency-sensitive client requests may be persisted in replicated NVRAM, and then later NAND, while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted. This allows the system to continue to serve client requests despite failures and component replacement. Although many hardware components contain unique identifiers that are visible to system administrators, manufacturer, hardware supply chain and ongoing monitoring quality control infrastructure, applications running on top of the infrastructure address virtualize addresses. These virtualized addresses do not change over the lifetime of the storage system, regardless of component failures and replacements. This allows each component of the storage system to be replaced over time without reconfiguration or disruptions of client request processing, i.e., the system supports non-disruptive upgrades.

In some embodiments, the virtualized addresses are stored with sufficient redundancy. A continuous monitoring system correlates hardware and software status and the hardware identifiers. This allows detection and prediction of failures due to faulty components and manufacturing details. The monitoring system also enables the proactive transfer of authorities and entities away from impacted devices before failure occurs by removing the component from the critical path in some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storage node 150 and contents of a non-volatile solid state storage 152 of the storage node 150. Data is communicated to and from the storage node 150 by a network interface controller (‘NIC’) 202 in some embodiments. Each storage node 150 has a CPU 156, and one or more non-volatile solid state storage 152, as discussed above. Moving down one level in FIG. 2C, each non-volatile solid state storage 152 has a relatively fast non-volatile solid state memory, such as nonvolatile random access memory (‘NVRAM’) 204, and flash memory 206. In some embodiments, NVRAM 204 may be a component that does not require program/erase cycles (DRAM, MRAM, PCM), and can be a memory that can support being written vastly more often than the memory is read from. Moving down another level in FIG. 2C, the NVRAM 204 is implemented in one embodiment as high speed volatile memory, such as dynamic random access memory (DRAM) 216, backed up by energy reserve 218. Energy reserve 218 provides sufficient electrical power to keep the DRAM 216 powered long enough for contents to be transferred to the flash memory 206 in the event of power failure. In some embodiments, energy reserve 218 is a capacitor, super-capacitor, battery, or other device, that supplies a suitable supply of energy sufficient to enable the transfer of the contents of DRAM 216 to a stable storage medium in the case of power loss. The flash memory 206 is implemented as multiple flash dies 222, which may be referred to as packages of flash dies 222 or an array of flash dies 222. It should be appreciated that the flash dies 222 could be packaged in any number of ways, with a single die per package, multiple dies per package (i.e. multichip packages), in hybrid packages, as bare dies on a printed circuit board or other substrate, as encapsulated dies, etc. In the embodiment shown, the non-volatile solid state storage 152 has a controller 212 or other processor, and an input output (I/O) port 210 coupled to the controller 212. I/O port 210 is coupled to the CPU 156 and/or the network interface controller 202 of the flash storage node 150. Flash input output (I/O) port 220 is coupled to the flash dies 222, and a direct memory access unit (DMA) 214 is coupled to the controller 212, the DRAM 216 and the flash dies 222. In the embodiment shown, the 1/O port 210, controller 212, DMA unit 214 and flash I/O port 220 are implemented on a programmable logic device (‘PLD’) 208, e.g., a field programmable gate array (FPGA). In this embodiment, each flash die 222 has pages, organized as sixteen kB (kilobyte) pages 224, and a register 226 through which data can be written to or read from the flash die 222. In further embodiments, other types of solid-state memory are used in place of, or in addition to flash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can be contrasted with storage arrays in general. The storage nodes 150 are part of a collection that creates the storage cluster 161. Each storage node 150 owns a slice of data and computing required to provide the data. Multiple storage nodes 150 cooperate to store and retrieve the data. Storage memory or storage devices, as used in storage arrays in general, are less involved with processing and manipulating the data. Storage memory or storage devices in a storage array receive commands to read, write, or erase data. The storage memory or storage devices in a storage array are not aware of a larger system in which they are embedded, or what the data means. Storage memory or storage devices in storage arrays can include various types of storage memory, such as RAM, solid state drives, hard disk drives, etc. The storage units 152 described herein have multiple interfaces active simultaneously and serving multiple purposes. In some embodiments, some of the functionality of a storage node 150 is shifted into a storage unit 152, transforming the storage unit 152 into a combination of storage unit 152 and storage node 150. Placing computing (relative to storage data) into the storage unit 152 places this computing closer to the data itself. The various system embodiments have a hierarchy of storage node layers with different capabilities. By contrast, in a storage array, a controller owns and knows everything about all of the data that the controller manages in a shelf or storage devices. In a storage cluster 161, as described herein, multiple controllers in multiple storage units 152 and/or storage nodes 150 cooperate in various ways (e.g., for erasure coding, data sharding, metadata communication and redundancy, storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments of the storage nodes 150 and storage units 152 of FIGS. 2A-C. In this version, each storage unit 152 has a processor such as controller 212 (see FIG. 2C), an FPGA (field programmable gate array), flash memory 206, and NVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS. 2B and 2C) on a PCIe (peripheral component interconnect express) board in a chassis 138 (see FIG. 2A). The storage unit 152 may be implemented as a single board containing storage, and may be the largest tolerable failure domain inside the chassis. In some embodiments, up to two storage units 152 may fail and the device will continue with no data loss.

The physical storage is divided into named regions based on application usage in some embodiments. The NVRAM 204 is a contiguous block of reserved memory in the storage unit 152 DRAM 216, and is backed by NAND flash. NVRAM 204 is logically divided into multiple memory regions written for two as spool (e.g., spool_region). Space within the NVRAM 204 spools is managed by each authority 168 independently. Each device provides an amount of storage space to each authority 168. That authority 168 further manages lifetimes and allocations within that space. Examples of a spool include distributed transactions or notions. When the primary power to a storage unit 152 fails, onboard super-capacitors provide a short duration of power hold up. During this holdup interval, the contents of the NVRAM 204 are flushed to flash memory 206. On the next power-on, the contents of the NVRAM 204 are recovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical “controller” is distributed across each of the blades containing authorities 168. This distribution of logical control is shown in FIG. 2D as a host controller 242, mid-tier controller 244 and storage unit controller(s) 246. Management of the control plane and the storage plane are treated independently, although parts may be physically co-located on the same blade. Each authority 168 effectively serves as an independent controller. Each authority 168 provides its own data and metadata structures, its own background workers, and maintains its own lifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane 254, compute and storage planes 256, 258, and authorities 168 interacting with underlying physical resources, using embodiments of the storage nodes 150 and storage units 152 of FIGS. 2A-C in the storage server environment of FIG. 2D. The control plane 254 is partitioned into a number of authorities 168 which can use the compute resources in the compute plane 256 to run on any of the blades 252. The storage plane 258 is partitioned into a set of devices, each of which provides access to flash 206 and NVRAM 204 resources.

In the compute and storage planes 256, 258 of FIG. 2E, the authorities 168 interact with the underlying physical resources (i.e., devices). From the point of view of an authority 168, its resources are striped over all of the physical devices. From the point of view of a device, it provides resources to all authorities 168, irrespective of where the authorities happen to run. Each authority 168 has allocated or has been allocated one or more partitions 260 of storage memory in the storage units 152, e.g. partitions 260 in flash memory 206 and NVRAM 204. Each authority 168 uses those allocated partitions 260 that belong to it, for writing or reading user data. Authorities can be associated with differing amounts of physical storage of the system. For example, one authority 168 could have a larger number of partitions 260 or larger sized partitions 260 in one or more storage units 152 than one or more other authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storage cluster, in accordance with some embodiments. In the elasticity structure, elasticity software is symmetric, i.e., each blade's compute module 270 runs the three identical layers of processes depicted in FIG. 2F. Storage managers 274 execute read and write requests from other blades 252 for data and metadata stored in local storage unit 152 NVRAM 204 and flash 206. Authorities 168 fulfill client requests by issuing the necessary reads and writes to the blades 252 on whose storage units 152 the corresponding data or metadata resides. Endpoints 272 parse client connection requests received from switch fabric 146 supervisory software, relay the client connection requests to the authorities 168 responsible for fulfillment, and relay the authorities' 168 responses to clients. The symmetric three-layer structure enables the storage system's high degree of concurrency. Elasticity scales out efficiently and reliably in these embodiments. In addition, elasticity implements a unique scale-out technique that balances work evenly across all resources regardless of client access pattern, and maximizes concurrency by eliminating much of the need for inter-blade coordination that typically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the compute modules 270 of a blade 252 perform the internal operations required to fulfill client requests. One feature of elasticity is that authorities 168 are stateless, i.e., they cache active data and metadata in their own blades' 252 DRAMs for fast access, but the authorities store every update in their NVRAM 204 partitions on three separate blades 252 until the update has been written to flash 206. All the storage system writes to NVRAM 204 are in triplicate to partitions on three separate blades 252 in some embodiments. With triple-mirrored NVRAM 204 and persistent storage protected by parity and Reed-Solomon RAID checksums, the storage system can survive concurrent failure of two blades 252 with no loss of data, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades 252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206 partitions are associated with authorities' 168 identifiers, not with the blades 252 on which they are running in some. Thus, when an authority 168 migrates, the authority 168 continues to manage the same storage partitions from its new location. When a new blade 252 is installed in an embodiment of the storage cluster, the system automatically rebalances load by: partitioning the new blade's 252 storage for use by the system's authorities 168, migrating selected authorities 168 to the new blade 252, starting endpoints 272 on the new blade 252 and including them in the switch fabric's 146 client connection distribution algorithm.

From their new locations, migrated authorities 168 persist the contents of their NVRAM 204 partitions on flash 206, process read and write requests from other authorities 168, and fulfill the client requests that endpoints 272 direct to them. Similarly, if a blade 252 fails or is removed, the system redistributes its authorities 168 among the system's remaining blades 252. The redistributed authorities 168 continue to perform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of a storage cluster, in accordance with some embodiments. Each authority 168 is exclusively responsible for a partition of the flash 206 and NVRAM 204 on each blade 252. The authority 168 manages the content and integrity of its partitions independently of other authorities 168. Authorities 168 compress incoming data and preserve it temporarily in their NVRAM 204 partitions, and then consolidate, RAID-protect, and persist the data in segments of the storage in their flash 206 partitions. As the authorities 168 write data to flash 206, storage managers 274 perform the necessary flash translation to optimize write performance and maximize media longevity. In the background, authorities 168 “garbage collect,” or reclaim space occupied by data that clients have made obsolete by overwriting the data. It should be appreciated that since authorities' 168 partitions are disjoint, there is no need for distributed locking to execute client and writes or to perform background functions.

The embodiments described herein may utilize various software, communication and/or networking protocols. In addition, the configuration of the hardware and/or software may be adjusted to accommodate various protocols. For example, the embodiments may utilize Active Directory, which is a database based system that provides authentication, directory, policy, and other services in a WINDOWS™ environment. In these embodiments, LDAP (Lightweight Directory Access Protocol) is one example application protocol for querying and modifying items in directory service providers such as Active Directory. In some embodiments, a network lock manager (‘NLM’) is utilized as a facility that works in cooperation with the Network File System (‘NFS’) to provide a System V style of advisory file and record locking over a network. The Server Message Block (‘SMB’) protocol, one version of which is also known as Common Internet File System (‘CIFS’), may be integrated with the storage systems discussed herein. SMP operates as an application-layer network protocol typically used for providing shared access to files, printers, and serial ports and miscellaneous communications between nodes on a network. SMB also provides an authenticated inter-process communication mechanism. AMAZON™ S3 (Simple Storage Service) is a web service offered by Amazon Web Services, and the systems described herein may interface with Amazon S3 through web services interfaces (REST (representational state transfer), SOAP (simple object access protocol), and BitTorrent). A RESTful API (application programming interface) breaks down a transaction to create a series of small modules. Each module addresses a particular underlying part of the transaction. The control or permissions provided with these embodiments, especially for object data, may include utilization of an access control list (‘ACL’). The ACL is a list of permissions attached to an object and the ACL specifies which users or system processes are granted access to objects, as well as what operations are allowed on given objects. The systems may utilize Internet Protocol version 6 (‘IPv6’), as well as IPv4, for the communications protocol that provides an identification and location system for computers on networks and routes traffic across the Internet. The routing of packets between networked systems may include Equal-cost multi-path routing (‘ECMP’), which is a routing strategy where next-hop packet forwarding to a single destination can occur over multiple “best paths” which tie for top place in routing metric calculations. Multi-path routing can be used in conjunction with most routing protocols, because it is a per-hop decision limited to a single router. The software may support Multi-tenancy, which is an architecture in which a single instance of a software application serves multiple customers. Each customer may be referred to as a tenant. Tenants may be given the ability to customize some parts of the application, but may not customize the application's code, in some embodiments. The embodiments may maintain audit logs. An audit log is a document that records an event in a computing system. In addition to documenting what resources were accessed, audit log entries typically include destination and source addresses, a timestamp, and user login information for compliance with various regulations. The embodiments may support various key management policies, such as encryption key rotation. In addition, the system may support dynamic root passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled for data communications with a cloud services provider 302 in accordance with some embodiments of the present disclosure. Although depicted in less detail, the storage system 306 depicted in FIG. 3A may be similar to the storage systems described above with reference to FIGS. 1A-1D and FIGS. 2A-2G. In some embodiments, the storage system 306 depicted in FIG. 3A may be embodied as a storage system that includes imbalanced active/active controllers, as a storage system that includes balanced active/active controllers, as a storage system that includes active/active controllers where less than all of each controller's resources are utilized such that each controller has reserve resources that may be used to support failover, as a storage system that includes fully active/active controllers, as a storage system that includes dataset-segregated controllers, as a storage system that includes dual-layer architectures with front-end controllers and back-end integrated storage controllers, as a storage system that includes scale-out clusters of dual-controller arrays, as well as combinations of such embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled to the cloud services provider 302 via a data communications link 304. The data communications link 304 may be embodied as a dedicated data communications link, as a data communications pathway that is provided through the use of one or data communications networks such as a wide area network (‘WAN’) or local area network (‘LAN’), or as some other mechanism capable of transporting digital information between the storage system 306 and the cloud services provider 302. Such a data communications link 304 may be fully wired, fully wireless, or some aggregation of wired and wireless data communications pathways. In such an example, digital information may be exchanged between the storage system 306 and the cloud services provider 302 via the data communications link 304 using one or more data communications protocols. For example, digital information may be exchanged between the storage system 306 and the cloud services provider 302 via the data communications link 304 using the handheld device transfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol (‘IP’), real-time transfer protocol (‘RTP’), transmission control protocol (‘TCP’), user datagram protocol (‘UDP’), wireless application protocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, for example, as a system and computing environment that provides services to users of the cloud services provider 302 through the sharing of computing resources via the data communications link 304. The cloud services provider 302 may provide on-demand access to a shared pool of configurable computing resources such as computer networks, servers, storage, applications and services, and so on. The shared pool of configurable resources may be rapidly provisioned and released to a user of the cloud services provider 302 with minimal management effort. Generally, the user of the cloud services provider 302 is unaware of the exact computing resources utilized by the cloud services provider 302 to provide the services. Although in many cases such a cloud services provider 302 may be accessible via the Internet, readers of skill in the art will recognize that any system that abstracts the use of shared resources to provide services to a user through any data communications link may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 may be configured to provide a variety of services to the storage system 306 and users of the storage system 306 through the implementation of various service models. For example, the cloud services provider 302 may be configured to provide services to the storage system 306 and users of the storage system 306 through the implementation of an infrastructure as a service (‘IaaS’) service model where the cloud services provider 302 offers computing infrastructure such as virtual machines and other resources as a service to subscribers. In addition, the cloud services provider 302 may be configured to provide services to the storage system 306 and users of the storage system 306 through the implementation of a platform as a service (‘PaaS’) service model where the cloud services provider 302 offers a development environment to application developers. Such a development environment may include, for example, an operating system, programming-language execution environment, database, web server, or other components that may be utilized by application developers to develop and run software solutions on a cloud platform. Furthermore, the cloud services provider 302 may be configured to provide services to the storage system 306 and users of the storage system 306 through the implementation of a software as a service (‘SaaS’) service model where the cloud services provider 302 offers application software, databases, as well as the platforms that are used to run the applications to the storage system 306 and users of the storage system 306, providing the storage system 306 and users of the storage system 306 with on-demand software and eliminating the need to install and run the application on local computers, which may simplify maintenance and support of the application. The cloud services provider 302 may be further configured to provide services to the storage system 306 and users of the storage system 306 through the implementation of an authentication as a service (‘AaaS’) service model where the cloud services provider 302 offers authentication services that can be used to secure access to applications, data sources, or other resources. The cloud services provider 302 may also be configured to provide services to the storage system 306 and users of the storage system 306 through the implementation of a storage as a service model where the cloud services provider 302 offers access to its storage infrastructure for use by the storage system 306 and users of the storage system 306. Readers will appreciate that the cloud services provider 302 may be configured to provide additional services to the storage system 306 and users of the storage system 306 through the implementation of additional service models, as the service models described above are included only for explanatory purposes and in no way represent a limitation of the services that may be offered by the cloud services provider 302 or a limitation as to the service models that may be implemented by the cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 may be embodied, for example, as a private cloud, as a public cloud, or as a combination of a private cloud and public cloud. In an embodiment in which the cloud services provider 302 is embodied as a private cloud, the cloud services provider 302 may be dedicated to providing services to a single organization rather than providing services to multiple organizations. In an embodiment where the cloud services provider 302 is embodied as a public cloud, the cloud services provider 302 may provide services to multiple organizations. Public cloud and private cloud deployment models may differ and may come with various advantages and disadvantages. For example, because a public cloud deployment involves the sharing of a computing infrastructure across different organization, such a deployment may not be ideal for organizations with security concerns, mission-critical workloads, uptime requirements demands, and so on. While a private cloud deployment can address some of these issues, a private cloud deployment may require on-premises staff to manage the private cloud. In still alternative embodiments, the cloud services provider 302 may be embodied as a mix of a private and public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciate that additional hardware components and additional software components may be necessary to facilitate the delivery of cloud services to the storage system 306 and users of the storage system 306. For example, the storage system 306 may be coupled to (or even include) a cloud storage gateway. Such a cloud storage gateway may be embodied, for example, as hardware-based or software-based appliance that is located on premise with the storage system 306. Such a cloud storage gateway may operate as a bridge between local applications that are executing on the storage array 306 and remote, cloud-based storage that is utilized by the storage array 306. Through the use of a cloud storage gateway, organizations may move primary iSCSI or NAS to the cloud services provider 302, thereby enabling the organization to save space on their on-premises storage systems. Such a cloud storage gateway may be configured to emulate a disk array, a block-based device, a file server, or other storage system that can translate the SCSI commands, file server commands, or other appropriate command into REST-space protocols that facilitate communications with the cloud services provider 302.

In order to enable the storage system 306 and users of the storage system 306 to make use of the services provided by the cloud services provider 302, a cloud migration process may take place during which data, applications, or other elements from an organization's local systems (or even from another cloud environment) are moved to the cloud services provider 302. In order to successfully migrate data, applications, or other elements to the cloud services provider's 302 environment, middleware such as a cloud migration tool may be utilized to bridge gaps between the cloud services provider's 302 environment and an organization's environment. Such cloud migration tools may also be configured to address potentially high network costs and long transfer times associated with migrating large volumes of data to the cloud services provider 302, as well as addressing security concerns associated with sensitive data to the cloud services provider 302 over data communications networks. In order to further enable the storage system 306 and users of the storage system 306 to make use of the services provided by the cloud services provider 302, a cloud orchestrator may also be used to arrange and coordinate automated tasks in pursuit of creating a consolidated process or workflow. Such a cloud orchestrator may perform tasks such as configuring various components, whether those components are cloud components or on-premises components, as well as managing the interconnections between such components. The cloud orchestrator can simplify the inter-component communication and connections to ensure that links are correctly configured and maintained.

In the example depicted in FIG. 3A, and as described briefly above, the cloud services provider 302 may be configured to provide services to the storage system 306 and users of the storage system 306 through the usage of a SaaS service model where the cloud services provider 302 offers application software, databases, as well as the platforms that are used to run the applications to the storage system 306 and users of the storage system 306, providing the storage system 306 and users of the storage system 306 with on-demand software and eliminating the need to install and run the application on local computers, which may simplify maintenance and support of the application. Such applications may take many forms in accordance with various embodiments of the present disclosure. For example, the cloud services provider 302 may be configured to provide access to data analytics applications to the storage system 306 and users of the storage system 306. Such data analytics applications may be configured, for example, to receive telemetry data phoned home by the storage system 306. Such telemetry data may describe various operating characteristics of the storage system 306 and may be analyzed, for example, to determine the health of the storage system 306, to identify workloads that are executing on the storage system 306, to predict when the storage system 306 will run out of various resources, to recommend configuration changes, hardware or software upgrades, workflow migrations, or other actions that may improve the operation of the storage system 306.

The cloud services provider 302 may also be configured to provide access to virtualized computing environments to the storage system 306 and users of the storage system 306. Such virtualized computing environments may be embodied, for example, as a virtual machine or other virtualized computer hardware platforms, virtual storage devices, virtualized computer network resources, and so on. Examples of such virtualized environments can include virtual machines that are created to emulate an actual computer, virtualized desktop environments that separate a logical desktop from a physical machine, virtualized file systems that allow uniform access to different types of concrete file systems, and many others.

For further explanation, FIG. 3B sets forth a diagram of a storage system 306 in accordance with some embodiments of the present disclosure. Although depicted in less detail, the storage system 306 depicted in FIG. 3B may be similar to the storage systems described above with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storage system may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include storage resources 308, which may be embodied in many forms. For example, in some embodiments the storage resources 308 can include nano-RAM or another form of nonvolatile random access memory that utilizes carbon nanotubes deposited on a substrate. In some embodiments, the storage resources 308 may include 3D crosspoint non-volatile memory in which bit storage is based on a change of bulk resistance, in conjunction with a stackable cross-gridded data access array. In some embodiments, the storage resources 308 may include flash memory, including single-level cell (‘SLC’) NAND flash, multi-level cell (‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-level cell (‘QLC’) NAND flash, and others. In some embodiments, the storage resources 308 may include non-volatile magnetoresistive random-access memory (‘MRAM’), including spin transfer torque (‘STT’) MRAM, in which data is stored through the use of magnetic storage elements. In some embodiments, the example storage resources 308 may include non-volatile phase-change memory (‘PCM’) that may have the ability to hold multiple bits in a single cell as cells can achieve a number of distinct intermediary states. In some embodiments, the storage resources 308 may include quantum memory that allows for the storage and retrieval of photonic quantum information. In some embodiments, the example storage resources 308 may include resistive random-access memory (‘ReRAM’) in which data is stored by changing the resistance across a dielectric solid-state material. In some embodiments, the storage resources 308 may include storage class memory (‘SCM’) in which solid-state nonvolatile memory may be manufactured at a high density using some combination of sub-lithographic patterning techniques, multiple bits per cell, multiple layers of devices, and so on. Readers will appreciate that other forms of computer memories and storage devices may be utilized by the storage systems described above, including DRAM, SRAM, EEPROM, universal memory, and many others. The storage resources 308 depicted in FIG. 3A may be embodied in a variety of form factors, including but not limited to, dual in-line memory modules (‘DIMMs’), non-volatile dual in-line memory modules (‘NVDIMMs’), M.2, U.2, and others.

The example storage system 306 depicted in FIG. 3B may implement a variety of storage architectures. For example, storage systems in accordance with some embodiments of the present disclosure may utilize block storage where data is stored in blocks, and each block essentially acts as an individual hard drive. Storage systems in accordance with some embodiments of the present disclosure may utilize object storage, where data is managed as objects. Each object may include the data itself, a variable amount of metadata, and a globally unique identifier, where object storage can be implemented at multiple levels (e.g., device level, system level, interface level). Storage systems in accordance with some embodiments of the present disclosure utilize file storage in which data is stored in a hierarchical structure. Such data may be saved in files and folders, and presented to both the system storing it and the system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as a storage system in which additional storage resources can be added through the use of a scale-up model, additional storage resources can be added through the use of a scale-out model, or through some combination thereof. In a scale-up model, additional storage may be added by adding additional storage devices. In a scale-out model, however, additional storage nodes may be added to a cluster of storage nodes, where such storage nodes can include additional processing resources, additional networking resources, and so on.

The storage system 306 depicted in FIG. 3B also includes communications resources 310 that may be useful in facilitating data communications between components within the storage system 306, as well as data communications between the storage system 306 and computing devices that are outside of the storage system 306. The communications resources 310 may be configured to utilize a variety of different protocols and data communication fabrics to facilitate data communications between components within the storage systems as well as computing devices that are outside of the storage system. For example, the communications resources 310 can include fibre channel (‘FC’) technologies such as FC fabrics and FC protocols that can transport SCSI commands over FC networks. The communications resources 310 can also include FC over ethernet (‘FCoE’) technologies through which FC frames are encapsulated and transmitted over Ethernet networks. The communications resources 310 can also include InfiniBand (‘IB’) technologies in which a switched fabric topology is utilized to facilitate transmissions between channel adapters. The communications resources 310 can also include NVM Express (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologies through which non-volatile storage media attached via a PCI express (‘PCIe’) bus may be accessed. The communications resources 310 can also include mechanisms for accessing storage resources 308 within the storage system 306 utilizing serial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces for connecting storage resources 308 within the storage system 306 to host bus adapters within the storage system 306, internet small computer systems interface (‘iSCSI’) technologies to provide block-level access to storage resources 308 within the storage system 306, and other communications resources that that may be useful in facilitating data communications between components within the storage system 306, as well as data communications between the storage system 306 and computing devices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processing resources 312 that may be useful in useful in executing computer program instructions and performing other computational tasks within the storage system 306. The processing resources 312 may include one or more application-specific integrated circuits (‘ASICs’) that are customized for some particular purpose as well as one or more central processing units (‘CPUs’). The processing resources 312 may also include one or more digital signal processors (‘DSPs’), one or more field-programmable gate arrays (‘FPGAs’), one or more systems on a chip (‘SoCs’), or other form of processing resources 312. The storage system 306 may utilize the storage resources 312 to perform a variety of tasks including, but not limited to, supporting the execution of software resources 314 that will be described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes software resources 314 that, when executed by processing resources 312 within the storage system 306, may perform various tasks. The software resources 314 may include, for example, one or more modules of computer program instructions that when executed by processing resources 312 within the storage system 306 are useful in carrying out various data protection techniques to preserve the integrity of data that is stored within the storage systems. Readers will appreciate that such data protection techniques may be carried out, for example, by system software executing on computer hardware within the storage system, by a cloud services provider, or in other ways. Such data protection techniques can include, for example, data archiving techniques that cause data that is no longer actively used to be moved to a separate storage device or separate storage system for long-term retention, data backup techniques through which data stored in the storage system may be copied and stored in a distinct location to avoid data loss in the event of equipment failure or some other form of catastrophe with the storage system, data replication techniques through which data stored in the storage system is replicated to another storage system such that the data may be accessible via multiple storage systems, data snapshotting techniques through which the state of data within the storage system is captured at various points in time, data and database cloning techniques through which duplicate copies of data and databases may be created, and other data protection techniques. Through the use of such data protection techniques, business continuity and disaster recovery objectives may be met as a failure of the storage system may not result in the loss of data stored in the storage system.

The software resources 314 may also include software that is useful in implementing software-defined storage (‘SDS’). In such an example, the software resources 314 may include one or more modules of computer program instructions that, when executed, are useful in policy-based provisioning and management of data storage that is independent of the underlying hardware. Such software resources 314 may be useful in implementing storage virtualization to separate the storage hardware from the software that manages the storage hardware.

The software resources 314 may also include software that is useful in facilitating and optimizing I/O operations that are directed to the storage resources 308 in the storage system 306. For example, the software resources 314 may include software modules that perform carry out various data reduction techniques such as, for example, data compression, data deduplication, and others. The software resources 314 may include software modules that intelligently group together I/O operations to facilitate better usage of the underlying storage resource 308, software modules that perform data migration operations to migrate from within a storage system, as well as software modules that perform other functions. Such software resources 314 may be embodied as one or more software containers or in many other ways.

Readers will appreciate that the various components depicted in FIG. 3B may be grouped into one or more optimized computing packages as converged infrastructures. Such converged infrastructures may include pools of computers, storage and networking resources that can be shared by multiple applications and managed in a collective manner using policy-driven processes. Such converged infrastructures may minimize compatibility issues between various components within the storage system 306 while also reducing various costs associated with the establishment and operation of the storage system 306. Such converged infrastructures may be implemented with a converged infrastructure reference architecture, with standalone appliances, with a software driven hyper-converged approach, or in other ways.

Readers will appreciate that the storage system 306 depicted in FIG. 3B may be useful for supporting various types of software applications. For example, the storage system 306 may be useful in supporting artificial intelligence applications, database applications, DevOps projects, electronic design automation tools, event-driven software applications, high performance computing applications, simulation applications, high-speed data capture and analysis applications, machine learning applications, media production applications, media serving applications, picture archiving and communication systems (‘PACS’) applications, software development applications, and many other types of applications by providing storage resources to such applications.

For further explanation, FIG. 4 sets forth an example interface used to present analyzed utilization patterns in a storage array according to embodiments of the present disclosure. The interface depicted in FIG. 4 may be embodied, for example, as a graphical user interface (‘GUI’) that is depicted on a tablet computer, laptop computer, smartphone, on a display device coupled to a desktop computer, and in other ways. Such an interface may be utilized by a support technician, system administrator, or other entity to review analyzed utilization patterns in one or more storage arrays.

The example interface depicted in FIG. 4 includes a menu (402) that enables users to select between different screens that may be presented via the interface to carry out different functions, view different information, and so on. In the example depicted in FIG. 4, an analytics tab (406) has been selected such that analytical information related to a first array and a second array is being presented via the interface. In particular, a first graph (404) related to an array referred to as “Array 1” is being displayed and a second graph (412) related to an array referred to as “Array 2” is being displayed. The two graphs (404, 412) contain information that is related to capacity utilization trends in the two arrays, including information that shows the percentage of capacity in each array that has been utilized over time as well as a projection of how much capacity in each array will be utilized at various points in the future.

In order to present information that shows the percentage of capacity in each array that has been utilized over time as well as a projection of how much capacity in each array will be utilized at various points in the future, each of the graphs (404, 412) depicted in FIG. 4 includes a past-future divider (416, 418) that serves as a demarcation between past behavior and project future behavior. That is, information in each graph (404, 412) that is to the left of the past-future divider (416, 418) represents measured usage of the storage array that is associated with the particular graph (404, 412), whereas information in each graph (404, 412) that is to the right of the past-future divider (416, 418) represents a projection of future usage of the storage array that is associated with the particular graph (404, 412).

The projection of future usage of a particular storage array may be determined in a number of ways. For example, the projection of future usage of a particular storage array may be determined by examining the measured usage of the storage array over a predetermined period of time in the past and projecting similar future usage in a linear fashion. For example, if the capacity utilization of a particular storage array increased by 12 TB over the course of the past year, the projection of the future usage of the particular storage array may indicate that capacity utilization of the particular storage array is expected to increase by 1 TB per month, even if the if the capacity utilization of the particular storage array increased by 12 TB over the course of the past year in a non-linear fashion. In an alternative embodiment, the projection of future usage of a particular storage array may be determined by examining the measured usage of the storage array over a predetermined period of time in the past and projecting similar future usage in the same pattern as past usage. Such an example is depicted in FIG. 4, as the first graph (404) projects that future usage of the storage array will occur in the same pattern as usage occurred during the previous year, where capacity utilization increases more rapidly in some periods than others.

The interface depicted in FIG. 4 also includes components that enable a user of the interface to establish a configurable window (410) for projecting future usage of a particular storage array. In the example depicted in FIG. 4, a configurable window (410) has been created that selects a particular 43 day period of past usage of the associated storage array. In such an example, future usage of the associated storage array is projected by assuming that every 43 days, the same usage pattern as occurred in the configurable window (410) repeats itself. Such a configurable window (410) may therefore be utilized to select a period of time in the past that the user of the interface has determined to be more indicative of expected utilization in the future. A user of the interface may determine that a specific period of past usage is more indicative of expected utilization in the future, for example, because the specific period of past usage represents a time where workloads that are similar to those that will be executed on the array in the future were being executed, because some portion of past usage represented a period of time where the array was being configured and data was being populated rather than representing a period of time where the array was available to service I/O requests from users of the storage array.

The interface depicted in FIG. 4 also includes textual predictive information (408, 414) in each of the graphs (404, 412), Such textual predictive information (408, 414) can include a brief summary of important information such as, for example, information describing when the array that is associated with a particular graph (404, 412) is expected to have exhausted all of its capacity based on expected utilization of the array in the future, information describing when the array that is associated with a particular graph (404, 412) is expected to hit a predetermined usage threshold based on expected utilization of the array in the future, and so on.

Readers will appreciate that while the example depicted in FIG. 4 illustrates an example where information related to the utilization of storage capacity in multiple arrays are displayed, as user of the interface may choose to have many other types of information displayed. For example, information related to the utilization of processing resources within one or more arrays may be displayed, including information that identifies historical usage patterns of processing resources as well as information that identifies projected usage patterns of processing resources. Similarly, information related to the utilization of processing resources or storage resources within one or more write buffer devices may be displayed, including information that identifies historical usage patterns of processing resources and storage resources within a write buffer device as well as information that identifies projected usage patterns of processing resources and storage resources in the write buffer device. Information related to the occurrence of I/O errors within one or more arrays or even within particular storage devices may be displayed, including information that identifies the historical occurrence of I/O errors as well as information that identifies the projected occurrence of I/O errors.

Readers will further appreciate that while the example depicted in FIG. 4 relates to the presentation of analyzed utilization patterns in a storage array, analyzing utilization patterns in a storage array according to embodiments of the present disclosure may be carried out by one or more modules executing on a particular storage array controller, by one or more modules executing on computing resources in a cloud computing environment, by one or more modules executing on computing resources such as a system management server or other server, and so on. In some embodiments, the one or more modules executing on a particular storage array controller execute, in some cases exclusively, on a secondary controller instead of a primary controller with a two or more storage array controller system such as storage arrays 102A and 102B depicted in FIG. 1A. Such modules may be configured, for example, to gather and store information describing the operation of one or more storage arrays, to extrapolate information describing the operation of one or more storage arrays from information such as usage logs, to retrieve information describing the operation of one or more storage arrays from a usage repository or other data source, and so on. The modules may be further configured to analyze the information describing the operation of one or more storage arrays to generate, for example, information describing current levels of resource utilization for one or more storage arrays or components contained therein. The current levels of resource utilization for one or more storage arrays or components contained therein may be include, for example, information describing the amount of storage capacity in a storage array that is being utilized, information describing the amount of processing capacity in a storage array that is being utilized, information describing average latency times for carrying out various I/O operations, information describing peak latency times for carrying out various I/O operations, information describing the average amount of IOPS that a storage array can perform, and many others.

The modules described above may be further configured to analyze the information describing the operation of one or more storage arrays to generate, for example, information describing resource utilizations trends. Such resource utilizations trends may be identified, for example, by quantifying the rate at which quantifiable aspects of system utilization are changing, by quantifying the pattern at which quantifiable aspects of system utilization have changed over a period of time, and so on. In such an example, the modules may be configured to identify more and less relevant periods of time for the purpose of identifying a time change to use to identify resource utilization trends. The modules may be configured to identify more and less relevant periods of time for the purpose of identifying a time change to use to identify resource utilization trends in a variety of ways. For example, the modules may examine relatively current I/O patterns for a particular storage array to characterize the types of workloads that are currently executing on the particular storage. Such I/O patterns may be embodied, for example, as an I/O pattern wherein the storage array is servicing a much larger number of write operations than read operations, as an I/O pattern wherein the storage array is servicing a much larger number of read operations than write operations, as an I/O pattern wherein the storage array is receiving write operations with relatively large payloads, as an I/O pattern wherein the storage array is receiving write operations with relatively small payloads, as an I/O pattern wherein data is remaining valid on the storage array for relatively long periods of time after being written to the array, as an I/O pattern wherein data is remaining valid on the storage array for relatively short periods of time after being written to the array, as I/O patterns where I/O operations tend to be more bursty, as I/O patterns where I/O operations tend to arrive in a relatively steady stream, and so on. In such an example, the modules may operate under the assumption that I/O patterns are unlikely to change and may therefore target periods of time in the past with similar I/O patterns (or give a higher weighting to such periods) to evaluate for the purpose of identifying utilization trends. As such, periods of time in the past that include dissimilar I/O patterns than the relatively recent I/O patterns experienced by a particular storage array may not be taken into consideration (or may be given less weight) when identifying resource utilization trends.

Readers will further appreciate that the modules described above may utilize information describing resource utilizations trends and information describing current levels of resource utilization to predict future levels of current levels of resource utilization. The future levels of current levels of resource utilization can be utilized, for example, to generate warnings or alerts to a system administrator when predicted levels of resource utilization may be undesirable, to facilitate taking corrective actions such as changing system configuration parameters, relocating workloads, performing operations such as garbage collection, or taking other actions when predicted levels of resource utilization are be undesirable.

For further explanation, FIG. 5 sets forth an example interface used to present analyzed utilization patterns in a storage array according to embodiments of the present disclosure. The interface depicted in FIG. 5 may be embodied, for example, as a GUI that is depicted on a tablet computer, laptop computer, smartphone, on a display device coupled to a desktop computer, and in other ways. Such an interface may be utilized by a support technician, system administrator, or other entity to review analyzed utilization patterns in one or more storage arrays.

The example interface depicted in FIG. 5 includes a menu (502) that enables users to select between different screens that may be presented via the interface to carry out different functions, view different information, and so on. In the example depicted in FIG. 5, an analytics tab (504) has been selected such that analytical information related to a first array and a second array is being presented via the interface. In particular, a statistics table (508) is depicted for five arrays. The statistics table (508) includes information such as the maximum latency for a read operation serviced by each array over a one hour period, the average latency for read operations serviced by each array over a one hour period, the maximum latency for a write operation serviced by each array over a one hour period, and the average latency for write operations serviced by each array over a one hour period. In such a way, the statistics table (508) depicts both the average case read latency and the worst case read latency exhibited by each of the storage arrays over a one hour period. Readers will appreciate that in other embodiments the statistics table (508) may include other information such as, for example, information describing the average case, worst case, and best case IOPS supported by each of the storage arrays over a period of time, information describing the average case, worst case, and best case bandwidth for I/O operations achieved by each of the storage arrays over a period of time, and so on. The user interface depicted in FIG. 5 also includes an entity type selection menu (506) that allows a user to select between different types of user-visible entities for displaying statistical information about. For example, the user interface depicted in FIG. 5 enables a user to view statistical information related to one or more volumes. Readers will appreciate that the statistical tables that are generated for different types of entities may include fewer, additional, or the same fields.

Readers will appreciate that while the example depicted in FIG. 5 relates to the presentation of analyzed utilization patterns in a storage array, analyzing utilization patterns in a storage array according to embodiments of the present disclosure may be carried out by one or more modules executing on a particular storage, by one or more modules executing on computing resources in a cloud computing environment, by one or more modules executing on computing resources such as a system management server or other server, and so on. In some embodiments, the one or more modules executing on a particular storage array controller execute, in some cases exclusively, on a secondary controller instead of a primary controller with a two or more storage array controller system such as storage arrays 102A and 102B depicted in FIG. 1A. Such modules may be configured, for example, to gather and store information describing the operation of one or more storage arrays, to extrapolate information describing the operation of one or more storage arrays from information such as usage logs, to retrieve information describing the operation of one or more storage arrays from a usage repository or other data source, and so on. The modules may be further configured to analyze the information describing the operation of one or more storage arrays to generate, for example, information describing current levels of resource utilization for one or more storage arrays or components contained therein. The current levels of resource utilization for one or more storage arrays or components contained therein may be include, for example, information describing the amount of storage capacity in a storage array that is being utilized, information describing the amount of processing capacity in a storage array that is being utilized, information describing average latency times for carrying out various I/O operations, information describing peak latency times for carrying out various I/O operations, information describing the average amount of IOPS that a storage array can perform, and many others.

For further explanation, FIG. 6 sets forth a flow chart illustrating an example method of analyzing utilization patterns in a storage array according to embodiments of the present disclosure. The example method depicted in FIG. 6 may be carried out, for example, by one or more modules of computer program instructions executing on computer hardware such as a CPU. The one or more modules of computer program instructions may be executing on computer hardware such as a CPU that is contained, for example, in a computing device (e.g., a storage array controller, a blade) that is included within the storage system, in a system management server or other server that monitors various activities of the storage system, in a computing device that is included within a cloud computing environment, and so on. Although depicted in less detail, the storage system 306 depicted in FIG. 6 may be similar to the storage systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A and 3B, or any combination thereof. Further, the storage system depicted in FIG. 6 may include the same, fewer, or additional components as the storage systems described above.

The example method depicted in FIG. 6 includes determining (602), for each of a plurality of entities in a storage system, a busyness factor. The plurality of entities in the storage system may be embodied, for example, as physical entities such as one or more computer processors in a storage array controller, as a data communications link between storage array controllers, as a data communications link over which storage array controllers receive I/O requests from users of the storage system, as one or more write buffer devices in the storage system, as one or more SSDs in the storage system, and so on. In such an example, the extent to which each of the entities in the storage system can perform their respective tasks may be bounded by performance maximums. For example, the one or more computer processors in a storage array controller may only execute a maximum number of commands over a period of time as the number of processing cycles that the computer processors can carry out is not unlimited. Likewise, the amount of data that can be transferred over a data communications link between storage array controllers during a period of time is not unlimited as the physical properties of the underlying materials do not permitted unlimited bandwidth. The amount of data that may be written to or read from one or more write buffer devices or one or more SSDs in the storage system is similarly limited as neither type of device has unlimited I/O bandwidth.

In the example method depicted in FIG. 6, the busyness factor 652 for each of the entities in the storage system may be determined (602), for example, by determining what percentage of each entities total bandwidth is being consumed during a sample period. Consider an example in which one of the computer processors in a particular storage array controller is capable of executing 3,000,000,000 clock cycles per second, but on average the computer processor is only executing computer program instructions during 1,500,000,000 of those clock cycles per second. In such an example, the busyness factor for the computer processor may be set to a value of 50%. Consider an additional example in which one of the storage array controllers is capable of servicing 5 GB/s of I/O operations, but on average the storage array controller is only servicing 4 GB/s of I/O operations. In such an example, the busyness factor for the storage array controller may be set to a value of 80%.

Readers will appreciate that although the examples in the preceding paragraph describe embodiments where a busyness factor is determined (602) for a single device, in other embodiments multiple devices may collectively form a single entity and a busyness factor may be determined (602) for the single entity that includes a plurality of devices. For example, a plurality of computer processors in the storage system may be collectively pooled into a single entity, such that busyness factor for such a pool of computer processors can represent the extent to which the collective processing resources of the storage system are being utilized. Likewise, all write buffer devices in the storage system may be collectively pooled into a single entity, such that busyness factor for such a pool of write buffer devices can represent the extent to which the collective write buffering resources of the storage system are being utilized.

The example method depicted in FIG. 6 includes determining (604), in dependence upon the busyness factor 652 for each of the plurality of entities in the storage system, a system busyness factor. Determining (604) a system busyness 654 factor in dependence upon the busyness factor for each of the plurality of entities in the storage system may be carried out, for example, by setting the system busyness factor to a value that matches the largest busyness factor for the plurality of entities in the storage system. Consider an example in which the busyness factor for a pool of computer processors is 100%, the busyness factor for a pool of write buffer devices is 65%, and the busyness factor for a pool of data communications links for receiving I/O operations is 55%. In such an example, the system busyness factor may be set to a value of 100%. Readers will appreciate that although the pool of write buffer devices and the pool of data communications links for receiving I/O operations have additional bandwidth, the system as a whole is maxed out as the pool of computer processors have no additional processing cycles available. As such, any additional I/O operations that the pool of data communications links could receive and send to the pool of computer processors would likely be queued up rather than executed in a more expeditious manner. Readers will appreciate that in other embodiments, the system busyness factor may be determined (604) in another way such as, for example, by determining a weighted average of the busyness factor for each of the plurality of entities in the storage system, by determining an unweighted average of the busyness factor for each of the plurality of entities in the storage system, and in other ways.

The example method depicted in FIG. 6 also includes utilizing (605) the determined system busyness factor, where utilization of the system busyness factor may include several responses, including initiating (606), in dependence upon the system busyness factor, one or more remedial measures, presenting (608) the busyness information, or both initiating (606) the one or more remedial measures in addition to presenting (608) the busyness information.

As noted above, the utilization (605) of the determined system busyness factor may include initiating (606), in dependence upon the system busyness factor, one or more remedial measures. The one or more remedial measures can include, for example, adjusting one or more system configuration parameters, initiating or terminating one or more processes, powering up or powering down one or more devices within the storage array, and so on. Consider the example described above in which the system busyness factor of a storage system is 100% by virtue of the busyness factor for a pool of computer processors being 100%. In such an example, the one or more remedial measures may include, for example, terminating non-essential or low priority processes to free up available processing cycles for more essential or high priority processes, by powering up any computer processors that are in a power conservation mode, by migrating workloads to other storage arrays that are less busy, and so on. Readers will appreciate that the remedial measures that are initiated (606) may be determined based on an examination of the busyness factors for each of the entities. For example, a first set of remedial measures may be initiated (606) when a first entity is characterized by the highest busyness factor whereas a second set of remedial measures may be initiated (606) when a second entity is characterized by the highest busyness factor. Readers will further appreciate that that the remedial measures that are initiated (606) may be determined based on an examination of the busyness factors for multiple entities. For example, a first set of remedial measures may be initiated (606) when a first set of entities are characterized by the highest busyness factors whereas a second set of remedial measures may be initiated (606) when a second set of entities are characterized by the highest busyness factors.

Readers will appreciate that although embodiments described above relate to embodiments where one or more remedial measures are initiated (606) in dependence upon the system busyness factor, in alternative embodiments the busyness factor of a single entity may trigger the initiation of one or more remedial measures. For example, when the busyness factor for a pool of computer processors is over a certain threshold, a remedial measure of powering up any computer processors that are in a power conservation mode may be initiated (606) regardless of whether any additional entities are characterized by a higher busyness factor.

As noted above, the utilization (605) of the determined system busyness factor may include presenting (608) busyness information. Presenting (608) busyness information may be carried out, for example, through the use of a GUI that is displayed on a tablet computer, laptop computer, smartphone, on a display device coupled to a desktop computer, and in other ways. Such a GUI may include, for example, the busyness factor for each entity in the storage system, a representation of the busyness factor for one or more entities at various points of time in the past, a projection of the busyness factor for one or more entities at various points of time in the future, the system busyness factor, a representation of the system busyness factor at various points of time in the past, a projection of the system busyness factor at various points of time in the future, and so on.

For further explanation, FIG. 7 sets forth an example interface used to present analyzed utilization patterns in a storage array according to embodiments of the present disclosure. The interface depicted in FIG. 7 may be embodied, for example, as a GUI that is depicted on a tablet computer, laptop computer, smartphone, on a display device coupled to a desktop computer, and in other ways. Such an interface may be utilized by a support technician, system administrator, or other entity to review analyzed utilization patterns in one or more storage arrays.

The example interface depicts three graphs (702, 704, 706) that illustrate a depiction of busyness information in a particular storage array. The three graphs (702, 704, 706) depicted in FIG. 7 depict information such as a maximum bottleneck (i.e., a maximum value for the busyness factor associated with an of the entities), as well as information describing the amount of frontend IOPS serviced by the storage array and frontend bandwidth achieved utilized by the storage array, although readers will appreciate that many other graphs may be depicted.

Two of the graphs (704, 706) depicted in FIG. 7 illustrate the extent to which different entities consume different resources through the use of different shading and coloring. The different entities could take many forms including, for example, a particular storage array user of the storage array, a particular process executing on the storage array, and many others. In such a way, the graphs (702, 704, 706) may depicted additional detail beyond general system resource utilization.

For further explanation, FIG. 8 sets forth a flow chart illustrating an example method of analyzing utilization patterns in a storage array according to embodiments of the present disclosure. The example method depicted in FIG. 8 may be carried out, for example, by one or more modules of computer program instructions executing on computer hardware such as a CPU. The one or more modules of computer program instructions may be executing on computer hardware such as a CPU that is contained, for example, in a computing device (e.g., a storage array controller, a blade) that is included within the storage system, in a system management server or other server that monitors various activities of the storage system, in a computing device that is included within a cloud computing environment, and so on. Although depicted in less detail, the storage system 306 depicted in FIG. 8 may be similar to the storage systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A and 3B, or any combination thereof. Further, the storage system depicted in FIG. 8 may include the same, fewer, or additional components as the storage systems described above.

The example method depicted in FIG. 8 includes identifying (802) one or more types of system resources in a storage array, depicted in FIG. 8 as resource types 852. The one or more types of system resources can include, for example, computing resources such as CPUs, storage resources such as SSDs, memory resources such as RAM, write caching resources such as a write buffer device, and many others. Readers will appreciate that by identifying (802) one or more types of system resources in a storage array, the storage array may be viewed and even graphically depicted as a multidimensional dimensional object, where each axis corresponds to a particular type of resource.

The example method depicted in FIG. 8 also includes determining (804), for the one or more types of system resources on a storage array, an amount of availability of the resource type. Determining (804) an amount of availability for the one or more types of system resources on the storage array may be carried out, for example, by determining an amount of processing cycles that can be provided by computing resources such the CPUs in the storage array, by determining the amount of storage capacity that may be provided by the storage resources such as SSDs in the storage array, by determining the number of IOPS that may be provided by the storage resources such as SSDs in the storage array, by determining an amount of data per unit of time that may be written to or read from by the storage resources such as SSDs in the storage array, and so on. In this example, the amount of availability is depicted in FIG. 8 as availability 854.

The example method depicted in FIG. 8 also includes identifying (806) one or more workload types 856 executing on the storage array. The one or more workload types may be specified in general terms such as, for example, a database application, a virtual desktop infrastructure, and many others. The workload types, to the extent that they are identifiable, may even be characterized at a finer level of granularity such as, for example, an Oracle™ database, an IBM™ DB2 database, and so on. Identifying (806) one or more workload types executing on the storage array may be carried out, for example, by determining an I/O pattern for the storage array and comparing the I/O pattern to I/O patterns exhibited by known workload types, by examining data stored on the storage array to identify metadata that is indicative of a particular workload type, by identifying characteristics (e.g., block size) of data to identify data characteristics that are indicative of a particular workload type, and so on.

The example method depicted in FIG. 8 also includes determining (808), for each of the one or more workload types supported by the storage array, an amount of system resources consumed by each instance of the workload type. In this example, the amount of system resources consumed by each instance of the workload type is depicted in FIG. 8 as consumed resources 858. Determining (808) an amount of system resources consumed by each instance of a particular workload type may be carried out, for example, by dividing the total amount of resources consumed by all instances of a particular workload type by the number of instances of the workload type. Consider an example where the workload type is a virtual machine. In such an example, determining (808) an amount of system resources consumed by each instance of a virtual machine may be carried out, for example, by dividing the total amount of resources consumed by all virtual machines by the number of virtual machines supported by the storage array.

Readers will appreciate that while the preceding paragraph determining (808) an amount of system resources generally that are consumed by each instance of one or more workload types that are supported by the storage array, determining (808) an amount of system resources utilized by an instance of a particular workload type may include determining, for one or more types of system resources on a storage array, an amount of the particular type of system resource that is utilized by an instance of a particular workload type. Continuing with the example where the workload type is a virtual machine, determining (808) an amount of system resources that are consumed by each instance of a virtual machine may include, for example, determining an amount of storage resources that are consumed by each virtual machine, determining an amount of processing resources that are consumed by each virtual machine, determining an amount of bandwidth resources that are consumed by each virtual machine, and so on.

The example method depicted in FIG. 8 also includes determining (810), for one or more workload types, an amount of additional instances of the workload type that can be supported by the storage array. In this example, the amount of additional instances of the workload type that can be supported by the storage array is depicted in FIG. 8 as resource availability 860. Determining (810) an amount of additional instances of a particular workload type that can be supported by the storage array may be carried out, for example, by dividing the amount of available system resources by the amount of system resources consumed by each instance of a particular workload type. In some embodiments, determining (810) an amount of additional instances of a particular workload type that can be supported by the storage array may include by dividing the amount of available system resources of each resource type by the amount of system resources of each resource type that is consumed by each instance of a particular workload type, and limiting the amount of additional instances of the workload type that can be supported by the storage array to the lowest value of amongst the various resource types.

Continuing with the example where the workload type is a virtual machine, assume that each instance of the virtual machine consumes 1 GB of storage resources, 1 MB of memory resources, and 1 MB/s of data transfer bandwidth. Further assume that the storage array has available 500 GB of storage resources, 400 MB of memory resources, and 300 MB/s of data transfer bandwidth. In such an example, the storage array has sufficient storage resources to support 500 additional virtual machines, but the storage array only has sufficient data transfer bandwidth to support an additional 300 virtual machines. As such, the amount of additional virtual machines that can be supported by the storage array will be determined (810) to be 300 virtual machines, as supporting any additional virtual machines would cause an oversubscription of data transfer bandwidth.

Readers will appreciate that in addition to determining (810) the amount of additional instances of each workload type that may supported by a storage array, the example method depicted in FIG. 8 may include determining various permutations of workload types that may be supported by the storage array. For example, rather than simply determining the amount of additional virtual machines that may be supported by the storage array and separately determining the amount of databases that may be supported by the storage array, all combinations of virtual machines and storage arrays that can be supported by the storage array may be supported.

Readers will appreciate that by determining (810) an amount of additional instances of a particular workload type that can be supported by the storage array, the availability of system resources may be expressed in terms that are more useful to a system administrator or other user. For example, the system administrator may find it more useful to be told that the storage array has sufficient system resources needed to support an additional 300 virtual machines, rather than being told that the storage array can support an additional 300 MB/s of data transfers. As such, information present via a GUI, alert message, report, or other format may be expressed in terms of the additional workloads that may be supported by a storage array.

Readers will further appreciate that by determining (804), for the one or more types of system resources on a storage array, an amount of availability of the resource type and by also determining (808), for each of the one or more workload supported by the storage array, an amount of system resources consumed by each instance of the workload type, workloads may be placed more intelligently in a multi-array environment. For example, workload types that consume relatively large amounts of storage resources and relatively small amounts of memory resources, may be placed on storage arrays that have relatively large amounts of storage resources available and relatively small amounts of memory resources available. To that end, the amount of each resource type that is available in a plurality of arrays may be compared to a resource utilization profile for a workload type to determine where to initially place a workload, to determine where to move a workload, to determine an optimal arrangement of a group of workloads, and so on.

For further explanation, FIG. 9 sets forth a flow chart illustrating an example method of performance tuning in a storage array according to embodiments of the present disclosure. The example method depicted in FIG. 9 may be carried out by one or more modules of computer program instructions executing on computer hardware such as a CPU. The one or more modules of computer program instructions may be executing on computer hardware such as a CPU that is contained, for example, in a computing device (e.g., a storage array controller such as a primary controller or a secondary controller, or both a primary and secondary controller, or a blade) that is included within the storage system, in a system management server or other server that monitors various activities of the storage system, in a computing device that is included within a cloud computing environment, and so on. Although depicted in less detail, the storage system 306 depicted in FIG. 9 may be similar to the storage systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A and 3B, or any combination thereof. Further, the storage system depicted in FIG. 9 may include the same, fewer, or additional components as the storage systems described above.

The example method depicted in FIG. 9 includes storing (902), by primary controller 110A of storage system 306, data 952 corresponding to one or more computer processes 950 into one or more storage devices 956A-956N. The one or more computer processes 950 may correspond to, or comprise, one or more software applications executing at different levels or layers, including one or more of: a user application layer, an operating system layer, a file system layer, or a data storage layer, where the data storage layer may include the operating environment that includes the one or more modules of storage system 306. Further, in this example, storing (902), by primary controller 110A of storage system 306, data 952 corresponding to, or received from, one or more computer processes 950 into one or more storage devices 956A-956N may be carried out by primary controller 110A detecting that one or more blocks, segments, or other unit of data are ready to be stored, and initiating a process for storing the data. For example, primary controller 110A may respond to a request to store data via an I/O operation, or may determine that data is available to be stored, for example data to be stored may be ready within a write buffer. Further, primary controller 110A may carry out initiating the storage of data by performing, directly through commands communicated to one or more storage devices or indirectly through commands communicated to an intermediate memory device such as non-volatile RAM, where in the indirect case the intermediate memory device completes the write to one or more storage devices. In some cases, the I/O operation may indicate a memory address, such as a virtual or logical memory location, and primary controller 110A translates the virtual memory location into a physical memory location within one or more of the storage devices 956A-956N.

The example method depicted in FIG. 9 also includes determining (904), by secondary controller 110B, one or more utilization patterns 954 of the data 952, where secondary controller 110B is configured similarly to primary controller 110A, and where the similar configuration includes secondary controller 110B being a replica of primary controller 110A. Determining (904) the one or more utilization patterns of the data 952 may be carried out in various ways. For example, each read or write of data may correspond to a respective I/O operation, where an I/O operation may include metadata describing the operation. This metadata in an I/O operation may be included in a header that indicates a type of operation such as a read or write, source identification indicating where the I/O operation originated, addressing information such as a volume identification and offset information, among other information characterizing the I/O operation. Further, for each I/O operation, storage system 306 may store or log metadata for I/O operations to determine metrics from which utilization information and trending information may be determined. For example, metrics, or I/O tracing information measuring quantities, frequencies, types, and sizes of I/O operations that are processed may be stored, tracked, and correlated to source entities corresponding to source information for each respective I/O operation. Further, in some example, the metrics or I/O tracing information may be correlated to one or more types of system resources used in handling given I/O operations.

Determining (904) the one or more utilization patterns of the data 952 may be carried out using one or more of the techniques described above with regard to analyzing workload patterns, client access patterns, and analyzing utilization patterns described above with regard to FIGS. 4-9. Further, examples of types of system resources are described above with reference to FIG. 6, and identifying (802) types of system resources. In other examples, the metrics or I/O tracing information may include workload types, where an I/O pattern may be determined for different storage devices for each workload type or for each source entity corresponding to source identification within an I/O operation header metadata. Examples of workload types are described above with reference to FIG. 6, and identifying (806) workload types. In this way, using one or more of these metrics or this I/O tracing information, patterns or trends that characterize usage of different portions of data may be determined or generated.

The example method depicted in FIG. 9 also includes initiating (906), in dependence upon the one or more utilization patterns 954 of the data, a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices 956A-956N, which may be carried out in various ways. For example, initiating (906), in dependence upon the one or more utilization patterns 954 of the data, a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices may be carried out by providing a notification indicating the one or more utilization patterns of the data to the one or more computer processes such that the receiving process or processes can use the utilization patterns to take corrective actions, perform optimizations, or to modify system resource utilization.

For example, carrying out initiating (906) a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices includes sending the utilization pattern information to a particular computer process, where the particular computer process may receive and use the utilization pattern information to modify a configurable resource that is a performance bottleneck. For example, the particular computer process may correspond to a resource management application, such as within a virtual management system, executing within a virtual computing environment using resources that include the one or more storage devices of storage system 306. Further, the resource management application may have authority to request more or fewer computing resources to be able to provide computation performance in accordance with, for example, one or more parameters of a service level agreement. In this example, the resource management application may use the utilization pattern information for the data being used by the resource management application, or being used by other computer processes within the virtual computing environment for which the resource management application may have administrative control, and determine that an increase in resource allocations would prevent failure to meet one or more service level agreement parameters. For example, the resource management application may determine that, based on the utilization patterns of the data, to make a request to increase an amount of available network bandwidth, to make a request to increase an amount of available storage or type of storage, or to make other combinations of resource modification requests in response to the utilization patterns of the data. For example, the resource management application may determine to make a request for a modification in resource allocation based on current usage metrics, based on trend information or projected usage metrics, based on resource availability, or based on combinations of each of the bases. In this example, current usage metrics may include the I/O tracing information described above with reference to FIG. 9 or the busyness information 652 and 654 described with reference to FIG. 6, the trend information or projected usage metrics may include the trend information described with reference to FIG. 4, and the resource availability may include the resource availability 860 information described with reference to FIG. 8.

In another example, carrying out initiating (906) a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices includes sending the utilization pattern information to a computer process, where the computer process may receive and use the utilization patterns of the data to request that the storage system 306 modify one or more configuration parameters, such as requesting the one or more remedial measures described above with regard to initiating (606) remedial measures described with reference to FIG. 6.

In another example, carrying out initiating (906) a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices includes a case where the one or more computer processes include a computer process that corresponds to a database application, a given modification to a manner in which the one or more computer processes 950 access the data may include a query optimization that results in better storage system 306 performance in satisfying a query result in contrast, or as compared to, a non-optimized query. Further, a given query optimization may be determined based at least in part on the one or more utilization patterns 954 of the data being accessed by, in this example, the database application.

For further explanation, FIG. 10 sets forth a flow chart illustrating an example method of performance tuning in a storage array according to embodiments of the present disclosure. The example method depicted in FIG. 10 may be carried out by one or more modules of computer program instructions executing on computer hardware such as a CPU. The one or more modules of computer program instructions may be executing on computer hardware such as a CPU that is contained, for example, in a computing device (e.g., a storage array controller such as a primary controller or a secondary controller, or both a primary and secondary controller, or a blade) that is included within the storage system, in a system management server or other server that monitors various activities of the storage system, in a computing device that is included within a cloud computing environment, and so on. Although depicted in less detail, the storage system 306 depicted in FIG. 10 may be similar to the storage systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A and 3B, or any combination thereof. Further, the storage system depicted in FIG. 10 may include the same, fewer, or additional components as the storage systems described above.

The example method depicted in FIG. 10 is similar to the example method depicted in FIG. 9, as the example method depicted in FIG. 10 also includes storing (902), by primary controller 110A of storage system 306, data 952 corresponding to one or more computer processes 950 into one or more storage devices 956A-956N, determining (904), by secondary controller 110B, one or more utilization patterns 954 of the data 952, where secondary controller 110B is configured similarly to primary controller 110A, and where the similar configuration includes secondary controller 110B being a replica of primary controller 110A, and initiating (906), in dependence upon the one or more utilization patterns 954 of the data, a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices 956A-956N.

However, the example method depicted in FIG. 10 further includes modifying (1002) one or more system configuration parameters of the one or more storage devices 956A-956N, which may be carried out in various ways. In one example, the one or more computer processes may include a computer process operating at the data storage layer of the storage system 306, and modifying (1002) the one or more system configuration parameters may be carried out by the secondary controller, or in some cases the primary controller, requesting the one or more remedial measures described above with regard to initiating (606) remedial measures described with reference to FIG. 6. For example, modifying (1002) the one or more system configuration parameters may be carried out by initiating or terminating one or more processes, powering up or powering down one or more devices within the storage array, moving data from one device to another, and so on. In one case, the one or more utilization pattern of the data may indicate that a utilization trend indicates an increase in a busyness factor for computer resources that are handling data I/O requests for the computer process. In such an example, the one or more remedial measures may include, for example, terminating non-essential or low priority processes to free up available processing cycles for more essential or high priority processes, by powering up any computer processors that are in a power conservation mode, by migrating workloads to other storage arrays that are less busy, and so on

For further explanation, FIG. 11 sets forth a flow chart illustrating an example method of performance tuning in a storage array according to embodiments of the present disclosure. The example method depicted in FIG. 11 may be carried out by one or more modules of computer program instructions executing on computer hardware such as a CPU. The one or more modules of computer program instructions may be executing on computer hardware such as a CPU that is contained, for example, in a computing device (e.g., a storage array controller such as a primary controller or a secondary controller, or both a primary and secondary controller, or a blade) that is included within the storage system, in a system management server or other server that monitors various activities of the storage system, in a computing device that is included within a cloud computing environment, and so on. Although depicted in less detail, the storage system 306 depicted in FIG. 11 may be similar to the storage systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A and 3B, or any combination thereof. Further, the storage system depicted in FIG. 11 may include the same, fewer, or additional components as the storage systems described above.

The example method depicted in FIG. 11 is similar to the example method depicted in FIG. 9, as the example method depicted in FIG. 11 also includes storing (902), by primary controller 110A of storage system 306, data 952 corresponding to one or more computer processes 950 into one or more storage devices 956A-956N, determining (904), by secondary controller 110B, one or more utilization patterns 954 of the data 952, where secondary controller 110B is configured similarly to primary controller 110A, and where the similar configuration includes secondary controller 110B being a replica of primary controller 110A, and initiating (906), in dependence upon the one or more utilization patterns 954 of the data, a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices 956A-956N.

However, the example method depicted in FIG. 11 further specifies that the one or more computer processes correspond to one or more software applications from one or more of a user application layer, an operating system or file system, layer, or a data storage layer. The example method depicted in FIG. 11 further includes sending (1102), from the secondary controller to an application programming interface (“API”) of one or more software applications, the one or more utilization patterns for the data 952, which may be carried out in various ways. For example, the one or more software applications may include a particular software application, and sending (1102) the one or more utilization patterns of the data may be carried out by a controller correlating a source identification with the data 952, where the source identification may be obtained from metadata such as header metadata for I/O operations that have read or written data 952, where the source identification corresponds to a particular software application, where the particular software application may have a corresponding API, and where the API provides a communication method or channel for sending or receiving information, including the utilization patterns for the data.

For further explanation, FIG. 12 sets forth a flow chart illustrating an example method of performance tuning in a storage array according to embodiments of the present disclosure. The example method depicted in FIG. 12 may be carried out by one or more modules of computer program instructions executing on computer hardware such as a CPU. The one or more modules of computer program instructions may be executing on computer hardware such as a CPU that is contained, for example, in a computing device (e.g., a storage array controller such as a primary controller or a secondary controller, or both a primary and secondary controller, or a blade) that is included within the storage system, in a system management server or other server that monitors various activities of the storage system, in a computing device that is included within a cloud computing environment, and so on. Although depicted in less detail, the storage system 306 depicted in FIG. 12 may be similar to the storage systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A and 3B, or any combination thereof. Further, the storage system depicted in FIG. 12 may include the same, fewer, or additional components as the storage systems described above.

The example method depicted in FIG. 12 is similar to the example method depicted in FIG. 9, as the example method depicted in FIG. 12 also includes storing (902), by primary controller 110A of storage system 306, data 952 corresponding to one or more computer processes 950 into one or more storage devices 956A-956N, determining (904), by secondary controller 110B, one or more utilization patterns 954 of the data 952, where secondary controller 110B is configured similarly to primary controller 110A, and where the similar configuration includes secondary controller 110B being a replica of primary controller 110A, and initiating (906), in dependence upon the one or more utilization patterns 954 of the data, a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices 956A-956N.

However, the example method depicted in FIG. 12 further includes creating (1202) a snapshot of data stored within the one or more storage devices, where the snapshot includes data corresponding to the one or more computer processes, and where the example method specifies that determining (904) the one or more utilization patterns includes one or more of: analyzing (1204) the snapshot 1252 to determine workload patterns, analyzing (1206) the snapshot 1252 to determine client access patterns, or analyzing (1208) the snapshot 1252 to determine tracing information.

In this example, creating (1202) a snapshot may be carried out as described above with reference to FIG. 3B, where data snapshotting techniques may capture the state of data at various points in time.

In this example, analyzing (1204) the snapshot to determine workload patterns, analyzing (1206) the snapshot to determine client access patterns, and analyzing (1208) the snapshot to determine the tracing information may be carried out as described above with regard to FIG. 9, where the techniques for determining (904) the utilization patterns are applied based on accessing the snapshot to access I/O information instead of an analysis of I/O operations as they are being received and processed or instead of an analysis of the data stored on the storage devices before a snapshot is created of the data.

For further explanation, FIG. 13 sets forth a flow chart illustrating an example method of performance tuning in a storage array according to embodiments of the present disclosure. The example method depicted in FIG. 13 may be carried out by one or more modules of computer program instructions executing on computer hardware such as a CPU. The one or more modules of computer program instructions may be executing on computer hardware such as a CPU that is contained, for example, in a computing device (e.g., a storage array controller such as a primary controller or a secondary controller, or both a primary and secondary controller, or a blade) that is included within the storage system, in a system management server or other server that monitors various activities of the storage system, in a computing device that is included within a cloud computing environment, and so on. Although depicted in less detail, the storage system 306 depicted in FIG. 13 may be similar to the storage systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A and 3B, or any combination thereof. Further, the storage system depicted in FIG. 13 may include the same, fewer, or additional components as the storage systems described above.

The example method depicted in FIG. 13 is similar to the example method depicted in FIG. 9, as the example method depicted in FIG. 13 also includes storing (902), by primary controller 110A of storage system 306, data 952 corresponding to one or more computer processes 950 into one or more storage devices 956A-956N, determining (904), by secondary controller 110B, one or more utilization patterns 954 of the data 952, where secondary controller 110B is configured similarly to primary controller 110A, and where the similar configuration includes secondary controller 110B being a replica of primary controller 110A, and initiating (906), in dependence upon the one or more utilization patterns 954 of the data, a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices 956A-956N.

However, the example method depicted in FIG. 12 further specifies that determining (904) the one or more utilization patterns includes evaluating (1302), by the secondary controller 110B, metadata corresponding to each input/output operation handled by the primary controller 110A to determine tracing information for the data 952. In one example, evaluating (1302) the metadata corresponding to each I/O operation handled by the primary controller may be carried out by the secondary controller implementing a module configured to snoop for I/O operations on one or more data communication links 106 within a storage array 102A. In other examples, storage system 306 may be configured such that all communications, or in some cases at least the I/O operations, received by primary controller 110A are also received by secondary controller 110B, as depicted in FIG. 1A, where primary controller 110A is configured to perform received I/O operations, and where secondary controller 110B is configured to perform I/O operations in the event of a failure of the primary controller 110A, and where in the absence of a failure of primary controller 110A, secondary controller 110B performs the operations described above with reference to FIG. 9, and to determining (904) the one or more utilization patterns of the data.

For further explanation, FIG. 14 sets forth a flow chart illustrating an example method of performance tuning in a storage array according to embodiments of the present disclosure. The example method depicted in FIG. 14 may be carried out by one or more modules of computer program instructions executing on computer hardware such as a CPU. The one or more modules of computer program instructions may be executing on computer hardware such as a CPU that is contained, for example, in a computing device (e.g., a storage array controller such as a primary controller or a secondary controller, or both a primary and secondary controller, or a blade) that is included within the storage system, in a system management server or other server that monitors various activities of the storage system, in a computing device that is included within a cloud computing environment, and so on. Although depicted in less detail, the storage system 306 depicted in FIG. 14 may be similar to the storage systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A and 3B, or any combination thereof. Further, the storage system depicted in FIG. 14 may include the same, fewer, or additional components as the storage systems described above.

The example method depicted in FIG. 14 is similar to the example method depicted in FIG. 9, as the example method depicted in FIG. 14 also includes storing (902), by primary controller 110A of storage system 306, data 952 corresponding to one or more computer processes 950 into one or more storage devices 956A-956N, determining (904), by secondary controller 110B, one or more utilization patterns 954 of the data 952, where secondary controller 110B is configured similarly to primary controller 110A, and where the similar configuration includes secondary controller 110B being a replica of primary controller 110A, and initiating (906), in dependence upon the one or more utilization patterns 954 of the data, a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices 956A-956N.

However, the example method depicted in FIG. 14 further includes determining (1402), in dependence upon the one or more utilization patterns of the data 952, a second storage location for one or more portions of data 1452 stored within a first storage location on the one or more storage device 956A-956N, and moving (1404) the one or more portions of data 1452 to the second storage location, where the second storage location has better performance characteristics than the first storage location on the one or more storage devices.

In one example, the one or more computer processes may correspond to a database application, and the data 952 corresponds to queried data of a database, and in this case, determining (1402), in dependence upon the one or more utilization patterns of the data 952, a second storage location for one or more portions of data 1452 stored within a first storage location on the one or more storage device 956A-956N may be carried out by the secondary controller determining that a database index is stored within the one or more portions of data 1452 and is being accessed above a threshold number of I/O operations over a period of time, where the secondary controller further determines that query handling would be improved if the database index were moved to faster memory, or memory that is configured to handle greater amounts of communication bandwidth.

In this example, responsive to determining (1402) the second storage location for the one or more portions of data 1452, the secondary controller 110B may move (1402) the one or more portions of data 1452, which may be carried out by copying the one or more portions of data 1452 from a storage device 956, to, for example, RAM, cache memory, or some other type of memory with better performance characteristics than storage device 956. As a consequence of such a copy of the one or more portions of data 1452, the database index is moved to faster memory, and database queries using the database index may be handled more efficiently, or quickly, than if the database index were stored on storage device 956.

In a similar manner, for other software applications that include the one or more computer processes 950, data accessed at a frequency above a configurable, or predetermined, threshold may be identified and moved from slower memory to faster memory to increase I/O operation throughput.

For further explanation, FIG. 15 sets forth a flow chart illustrating an example method of performance tuning in a storage array according to embodiments of the present disclosure. The example method depicted in FIG. 15 may be carried out by one or more modules of computer program instructions executing on computer hardware such as a CPU. The one or more modules of computer program instructions may be executing on computer hardware such as a CPU that is contained, for example, in a computing device (e.g., a storage array controller such as a primary controller or a secondary controller, or both a primary and secondary controller, or a blade) that is included within the storage system, in a system management server or other server that monitors various activities of the storage system, in a computing device that is included within a cloud computing environment, and so on. Although depicted in less detail, the storage system 306 depicted in FIG. 15 may be similar to the storage systems described above with reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A and 3B, or any combination thereof. Further, the storage system depicted in FIG. 15 may include the same, fewer, or additional components as the storage systems described above.

The example method depicted in FIG. 15 is similar to the example method depicted in FIG. 9, as the example method depicted in FIG. 15 also includes storing (902), by primary controller 110A of storage system 306, data 952 corresponding to one or more computer processes 950 into one or more storage devices 956A-956N, determining (904), by secondary controller 110B, one or more utilization patterns 954 of the data 952, where secondary controller 110B is configured similarly to primary controller 110A, and where the similar configuration includes secondary controller 110B being a replica of primary controller 110A, and initiating (906), in dependence upon the one or more utilization patterns 954 of the data, a modification to a manner in which the one or more computer processes 950 access the data stored in the one or more storage devices 956A-956N.

However, the example method depicted in FIG. 15 further includes, for a section of data stored within the one or more storage devices 956A-956N, reverse mapping (1502) the section of data to a software application, where the data corresponding to the one or more computer processes includes the section of data, and sending (1504), in dependence upon reverse mapping (1502) the section of data, the utilization patterns 954 of the of the data to the software application.

In some examples, the secondary controller may create, update, and maintain an index that maps for sections of data written by each I/O operation to a source identification of an I/O operation, where the source identification is mapped to a software application. Further, the secondary controller may aggregate, in generating the utilization patterns of data, all metrics and I/O tracing information for data corresponding to a given software application or computer process. In this way, the index may correlate each section of data stored within the one or more storage devices 956A-956N to a specific software application, where the utilization patterns correspond to all data used by a given software application. Further, the secondary controller may query, for example through an API, an operating system for a software application that corresponds to a source identification for a given I/O operation. In this way, reverse mapping (1502) the section of data to a software application may be carried out by the secondary controller referencing the index to identify a given software application that is mapped to a given section of data. Further, the secondary controller, given the identified software application, may query an operating system to identify API information for the identified software application to generate software application API data 1552.

Sending (1504), in dependence upon reverse mapping (1502) the section of data, a notification 1554 to the software application, the utilization patterns of the section of data to the software application may be carried out by the secondary controller using the software application API data 1552 to send and receive communications to the software application. In some cases, the secondary controller may send the utilization patterns of the data on a periodic or aperiodic basis.

Readers will appreciate that although the many of the examples depicted in the Figures described above relate to various embodiments of the present disclosure, other embodiments are well within the scope of the present disclosure. In particular, steps depicted in one figure may be combined with steps depicted in other figures to create permutations of the embodiments expressly called out in the figures. Readers will further appreciate that although the example methods described above are depicted in a way where a series of steps occurs in a particular order, no particular ordering of the steps is required unless explicitly stated.

Example embodiments are described largely in the context of a fully functional computer system for the methods described above executing on a storage system. Readers of skill in the art will recognize, however, that the present disclosure also may be embodied in a computer program product disposed upon computer readable storage media for use with any suitable data processing system. Such computer readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of such media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art. Persons skilled in the art will immediately recognize that any computer system having suitable programming means will be capable of executing the steps of the method as embodied in a computer program product. Persons skilled in the art will recognize also that, although some of the example embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present disclosure.

Embodiments can include be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to some embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

Readers will appreciate that the steps described herein may be carried out in a variety ways and that no particular ordering is required. It will be further understood from the foregoing description that modifications and changes may be made in various embodiments of the present disclosure without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present disclosure is limited only by the language of the following claims. 

1. A method of performance tuning in a storage system that includes one or more storage devices, the method comprising: storing, by a primary controller of the storage system, first data of a first workload type and second data of a second workload type into one or more of the one or more storage devices; determining, by a secondary controller that is configured similarly to the primary controller, a first utilization pattern associated with the first workload type and a second utilization pattern associated with the second workload type; and initiating, in dependence upon the first utilization pattern and the second utilization pattern, a modification to a manner in which one or more computer processes access the first data and the second data stored in the one or more storage devices.
 2. The method of claim 1, wherein the one or more computer processes correspond to one or more software applications from one or more of: a user application layer, an operating system layer, or a data storage layer.
 3. The method of claim 2, wherein at least one of the one or more computer processes is a process at the data storage layer, and wherein initiating the modification to the manner in which the one or more computer processes access the first data and the second data comprises: modifying one or more system configuration parameters of the one or more storage devices.
 4. The method of claim 2, wherein initiating the modification to the manner in which the one or more computer processes access the first data and the second data comprises: sending, from the secondary controller to an application programming interface of the one or more software applications, the first utilization pattern and the second utilization pattern.
 5. The method of claim 4, wherein at least one of the one or more software applications is a management application for a virtual computing environment, and wherein the first utilization pattern and the second utilization pattern each correspond to one or more configurable virtual or physical resource constraints.
 6. The method of claim 1, further comprising: creating a snapshot of data stored within the one or more storage devices, wherein the snapshot includes the data corresponding to the one or more computer processes; wherein determining, by the secondary controller, the first utilization pattern and the second utilization pattern comprises analyzing the snapshot to determine one or more of: workload patterns, client access patterns, or tracing information.
 7. The method of claim 1, wherein determining, by the secondary controller, the first utilization pattern and the second utilization pattern comprises: evaluating, by the secondary controller, metadata corresponding to each input/output (I/O) operation handled by the primary controller to determine tracing information.
 8. The method of claim 7, wherein the tracing information includes one or more of: an indication that a given IO operation is a read operation, an indication that a given IO operation is a write operation, an indication of a source entity for a given IO operation, an indication of a volume identification for a target volume of a given IO operation, an indication of addressing information for a given I/O operation, or an indication of a memory size for a given I/O operation.
 9. The method of claim 1, further comprising: determining, in dependence upon one or more utilization patterns, a second storage location for one or more portions of data stored within a first storage location on the one or more storage volumes; and moving the one or more portions of the data to the second storage location, wherein the second storage location that has better performance characteristics than the first storage location on the one or more storage volumes.
 10. The method of claim 1, further comprising: for a section of data stored within the one or more storage devices, reverse mapping the section of data to a software application, wherein the data corresponding to the one or more computer processes includes the section of data; and sending, in dependence upon reverse mapping the section of data, one or more utilization patterns to the software application.
 11. An apparatus that includes one or more storage devices, the apparatus including a computer processor and a computer memory, the computer memory including computer program instructions that, when executed by the computer processor, cause the computer processor to carry out the steps of: storing, by a primary controller of the storage system, first data of a first workload type and second data of a second workload type into one or more of the one or more storage devices; determining, by a secondary controller that is configured similarly to the primary controller, a first utilization pattern associated with the first workload type and a second utilization pattern associated with the second workload type; and initiating, in dependence upon the first utilization pattern and the second utilization pattern, a modification to a manner in which one or more computer processes access the first data and the second data stored in the one or more storage devices.
 12. The apparatus of claim 11, wherein the one or more computer processes correspond to one or more software applications from one or more of: a user application layer, an operating system layer, or a data storage layer.
 13. The apparatus of claim 12, wherein at least one of the one or more computer processes is a process at the data storage layer, and wherein initiating the modification to the manner in which the one or more computer processes access the first data and the second data comprises: modifying one or more system configuration parameters of the one or more storage devices.
 14. The apparatus of claim 12, wherein initiating the modification to the manner in which the one or more computer processes access the first data and the second data comprises: sending, from the secondary controller to an application programming interface of the one or more software applications, the first utilization pattern and the second utilization pattern.
 15. The apparatus of claim 14, wherein at least one of the one or more software applications is a management application for a virtual computing environment, and wherein the first utilization pattern and the second utilization pattern each correspond to one or more configurable virtual or physical resource constraints.
 16. The apparatus of claim 11, wherein the computer program instructions, when executed by the computer processor, cause the computer processor to further carry out the steps of: creating a snapshot of data stored within the one or more storage devices, wherein the snapshot includes the data corresponding to the one or more computer processes; wherein determining, by the secondary controller, the first utilization pattern and the second utilization pattern comprises analyzing the snapshot to determine one or more of: workload patterns, client access patterns, or tracing information.
 17. The apparatus of claim 16, wherein determining, by the secondary controller, the first utilization pattern and the second utilization pattern comprises: evaluating, by the secondary controller, metadata corresponding to each input/output (I/O) operation handled by the primary controller to determine tracing information.
 18. The apparatus of claim 17, wherein the tracing information includes one or more of: an indication that a given IO operation is a read operation, an indication that a given I/O operation is a write operation, an indication of a source entity for a given IO operation, an indication of a volume identification for a target volume of a given IO operation, an indication of addressing information for a given I/O operation, or an indication of a memory size for a given I/O operation.
 19. The apparatus of claim 11, wherein the computer program instructions, when executed by the computer processor, cause the computer processor to further carry out the steps of: determining, in dependence upon one or more utilization patterns, a second storage location for one or more portions of data stored within a first storage location on the one or more storage volumes; and moving the one or more portions of the data to the second storage location, wherein the second storage location that has better performance characteristics than the first storage location on the one or more storage volumes.
 20. The apparatus of claim 12, wherein the computer program instructions, when executed by the computer processor, cause the computer processor to further carry out the steps of: for a section of data stored within the one or more storage devices, reverse mapping the section of data to a software application, wherein the data corresponding to the one or more computer processes includes the section of data; and sending, in dependence upon reverse mapping the section of data, one or more utilization patterns to the software application. 